m tech thesis on machine learning

Available Master's thesis topics in machine learning

Main content.

Here we list topics that are available. You may also be interested in our list of completed Master's theses .

Learning and inference with large Bayesian networks

Most learning and inference tasks with Bayesian networks are NP-hard. Therefore, one often resorts to using different heuristics that do not give any quality guarantees.

Task: Evaluate quality of large-scale learning or inference algorithms empirically.

Advisor: Pekka Parviainen

Sum-product networks

Traditionally, probabilistic graphical models use a graph structure to represent dependencies and independencies between random variables. Sum-product networks are a relatively new type of a graphical model where the graphical structure models computations and not the relationships between variables. The benefit of this representation is that inference (computing conditional probabilities) can be done in linear time with respect to the size of the network.

Potential thesis topics in this area: a) Compare inference speed with sum-product networks and Bayesian networks. Characterize situations when one model is better than the other. b) Learning the sum-product networks is done using heuristic algorithms. What is the effect of approximation in practice?

Bayesian Bayesian networks

The naming of Bayesian networks is somewhat misleading because there is nothing Bayesian in them per se; A Bayesian network is just a representation of a joint probability distribution. One can, of course, use a Bayesian network while doing Bayesian inference. One can also learn Bayesian networks in a Bayesian way. That is, instead of finding an optimal network one computes the posterior distribution over networks.

Task: Develop algorithms for Bayesian learning of Bayesian networks (e.g., MCMC, variational inference, EM)

Large-scale (probabilistic) matrix factorization

The idea behind matrix factorization is to represent a large data matrix as a product of two or more smaller matrices.They are often used in, for example, dimensionality reduction and recommendation systems. Probabilistic matrix factorization methods can be used to quantify uncertainty in recommendations. However, large-scale (probabilistic) matrix factorization is computationally challenging.

Potential thesis topics in this area: a) Develop scalable methods for large-scale matrix factorization (non-probabilistic or probabilistic), b) Develop probabilistic methods for implicit feedback (e.g., recommmendation engine when there are no rankings but only knowledge whether a customer has bought an item)

Bayesian deep learning

Standard deep neural networks do not quantify uncertainty in predictions. On the other hand, Bayesian methods provide a principled way to handle uncertainty. Combining these approaches leads to Bayesian neural networks. The challenge is that Bayesian neural networks can be cumbersome to use and difficult to learn.

The task is to analyze Bayesian neural networks and different inference algorithms in some simple setting.

Deep learning for combinatorial problems

Deep learning is usually applied in regression or classification problems. However, there has been some recent work on using deep learning to develop heuristics for combinatorial optimization problems; see, e.g., [1] and [2].

Task: Choose a combinatorial problem (or several related problems) and develop deep learning methods to solve them.

References: [1] Vinyals, Fortunato and Jaitly: Pointer networks. NIPS 2015. [2] Dai, Khalil, Zhang, Dilkina and Song: Learning Combinatorial Optimization Algorithms over Graphs. NIPS 2017.

Advisors: Pekka Parviainen, Ahmad Hemmati

Estimating the number of modes of an unknown function

Mode seeking considers estimating the number of local maxima of a function f. Sometimes one can find modes by, e.g., looking for points where the derivative of the function is zero. However, often the function is unknown and we have only access to some (possibly noisy) values of the function. 

In topological data analysis,  we can analyze topological structures using persistent homologies. For 1-dimensional signals, this can translate into looking at the birth/death persistence diagram, i.e. the birth and death of connected topological components as we expand the space around each point where we have observed our function. These observations turn out to be closely related to the modes (local maxima) of the function. A recent paper [1] proposed an efficient method for mode seeking.

In this project, the task is to extend the ideas from [1] to get a probabilistic estimate on the number of modes. To this end, one has to use probabilistic methods such as Gaussian processes.

[1] U. Bauer, A. Munk, H. Sieling, and M. Wardetzky. Persistence barcodes versus Kolmogorov signatures: Detecting modes of one-dimensional signals. Foundations of computational mathematics17:1 - 33, 2017.

Advisors:  Pekka Parviainen ,  Nello Blaser

Causal Abstraction Learning

We naturally make sense of the world around us by working out causal relationships between objects and by representing in our minds these objects with different degrees of approximation and detail. Both processes are essential to our understanding of reality, and likely to be fundamental for developing artificial intelligence. The first process may be expressed using the formalism of structural causal models, while the second can be grounded in the theory of causal abstraction.        

This project will consider the problem of learning an abstraction between two given structural causal models. The primary goal will be the development of efficient algorithms able to learn a meaningful abstraction between the given causal models.

Advisor: Fabio Massimo Zennaro

Causal Bandits

"Multi-armed bandit" is an informal name for slot machines, and the formal name of a large class of problems where an agent has to choose an action among a range of possibilities without knowing the ensuing rewards. Multi-armed bandit problems are one of the most essential reinforcement learning problems where an agent is directly faced with an exploitation-exploration trade-off.

This project will consider a class of multi-armed bandits where an agent, upon taking an action, interacts with a causal system. The primary goal will be the development of learning strategies that takes advantage of the underlying causal system in order to learn optimal policies in a shortest amount of time.

Causal Modelling for Battery Manufacturing

Lithium-ion batteries are poised to be one of the most important sources of energy in the near future. Yet, the process of manufacturing these batteries is very hard to model and control. Optimizing the different phases of production to maximize the lifetime of the batteries is a non-trivial challenge since physical models are limited in scope and collecting experimental data is extremely expensive and time-consuming.        

This project will consider the problem of aggregating and analyzing data regarding a few stages in the process of battery manufacturing. The primary goal will be the development of algorithms for transporting and integrating data collected in different contexts, as well as the use of explainable algorithms to interpret them.

Reinforcement Learning for Computer Security

The field of computer security presents a wide variety of challenging problems for artificial intelligence and autonomous agents. Guaranteeing the security of a system against attacks and penetrations by malicious hackers has always been a central concern of this field, and machine learning could now offer a substantial contribution. Security capture-the-flag simulations are particularly well-suited as a testbed for the application and development of reinforcement learning algorithms.

This project will consider the use of reinforcement learning for the preventive purpose of testing systems and discovering vulnerabilities before they can be exploited. The primary goal will be the modelling of capture-the-flag challenges of interest and the development of reinforcement learning algorithms that can solve them.

Approaches to AI Safety

The world and the Internet are more and more populated by artificial autonomous agents carrying out tasks on our behalf. Many of these agents are provided with an objective and they learn their behaviour trying to achieve their objective as best as they can. However, this approach can not guarantee that an agent, while learning its behaviour, will not undertake actions that may have unforeseen and undesirable effects. Research in AI safety tries to design autonomous agent that will behave in a predictable and safe way. 

This project will consider specific problems and novel solution in the domain of AI safety and reinforcement learning. The primary goal will be the development of innovative algorithms and their implementation withing established frameworks.

Reinforcement Learning for Super-modelling

Super-modelling [1] is a technique designed for combining together complex dynamical models: pre-trained models are aggregated with messages and information being exchanged in order synchronize the behavior  of the different modles and produce more accurate and reliable predictions. Super-models are used, for instance, in weather or climate science, where pre-existing models are ensembled together and their states dynamically aggregated to generate more realistic simulations. 

This project will consider how reinforcement learning algorithms may be used to solve the coordination problem among the individual models forming a super-model. The primary goal will be the formulation of the super-modelling problem within the reinforcement learning framework and the study of custom RL algorithms to improve the overall performance of super-models.

[1] Schevenhoven, Francine, et al. "Supermodeling: improving predictions with an ensemble of interacting models." Bulletin of the American Meteorological Society 104.9 (2023): E1670-E1686.

Advisor: Fabio Massimo Zennaro ,  Francine Janneke Schevenhoven

The Topology of Flight Paths

Air traffic data tells us the position, direction, and speed of an aircraft at a given time. In other words, if we restrict our focus to a single aircraft, we are looking at a multivariate time-series. We can visualize the flight path as a curve above earth's surface quite geometrically. Topological data analysis (TDA) provides different methods for analysing the shape of data. Consequently, TDA may help us to extract meaningful features from the air traffic data. Although the typical flight path shapes may not be particularly intriguing, we can attempt to identify more intriguing patterns or “abnormal” manoeuvres, such as aborted landings, go-arounds, or diverts.

Advisor:  Odin Hoff Gardå , Nello Blaser

Automatic hyperparameter selection for isomap

Isomap is a non-linear dimensionality reduction method with two free hyperparameters (number of nearest neighbors and neighborhood radius). Different hyperparameters result in dramatically different embeddings. Previous methods for selecting hyperparameters focused on choosing one optimal hyperparameter. In this project, you will explore the use of persistent homology to find parameter ranges that result in stable embeddings. The project has theoretic and computational aspects.

Advisor: Nello Blaser

Validate persistent homology

Persistent homology is a generalization of hierarchical clustering to find more structure than just the clusters. Traditionally, hierarchical clustering has been evaluated using resampling methods and assessing stability properties. In this project you will generalize these resampling methods to develop novel stability properties that can be used to assess persistent homology. This project has theoretic and computational aspects.

Topological Ancombs quartet

This topic is based on the classical Ancombs quartet and families of point sets with identical 1D persistence ( https://arxiv.org/abs/2202.00577 ). The goal is to generate more interesting datasets using the simulated annealing methods presented in ( http://library.usc.edu.ph/ACM/CHI%202017/1proc/p1290.pdf ). This project is mostly computational.

Persistent homology vectorization with cycle location

There are many methods of vectorizing persistence diagrams, such as persistence landscapes, persistence images, PersLay and statistical summaries. Recently we have designed algorithms to in some cases efficiently detect the location of persistence cycles. In this project, you will vectorize not just the persistence diagram, but additional information such as the location of these cycles. This project is mostly computational with some theoretic aspects.

Divisive covers

Divisive covers are a divisive technique for generating filtered simplicial complexes. They original used a naive way of dividing data into a cover. In this project, you will explore different methods of dividing space, based on principle component analysis, support vector machines and k-means clustering. In addition, you will explore methods of using divisive covers for classification. This project will be mostly computational.

Learning Acquisition Functions for Cost-aware Bayesian Optimization

This is a follow-up project of an earlier Master thesis that developed a novel method for learning Acquisition Functions in Bayesian Optimization through the use of Reinforcement Learning. The goal of this project is to further generalize this method (more general input, learned cost-functions) and apply it to hyperparameter optimization for neural networks.

Advisors: Nello Blaser , Audun Ljone Henriksen

Stable updates

This is a follow-up project of an earlier Master thesis that introduced and studied empirical stability in the context of tree-based models. The goal of this project is to develop stable update methods for deep learning models. You will design sevaral stable methods and empirically compare them (in terms of loss and stability) with a baseline and with one another.

Advisors:  Morten Blørstad , Nello Blaser

Multimodality in Bayesian neural network ensembles

One method to assess uncertainty in neural network predictions is to use dropout or noise generators at prediction time and run every prediction many times. This leads to a distribution of predictions. Informatively summarizing such probability distributions is a non-trivial task and the commonly used means and standard deviations result in the loss of crucial information, especially in the case of multimodal distributions with distinct likely outcomes. In this project, you will analyze such multimodal distributions with mixture models and develop ways to exploit such multimodality to improve training. This project can have theoretical, computational and applied aspects.

Learning a hierarchical metric

Often, labels have defined relationships to each other, for instance in a hierarchical taxonomy. E.g. ImageNet labels are derived from the WordNet graph, and biological species are taxonomically related, and can have similarities depending on life stage, sex, or other properties.

ArcFace is an alternative loss function that aims for an embedding that is more generally useful than softmax. It is commonly used in metric learning/few shot learning cases.

Here, we will develop a metric learning method that learns from data with hierarchical labels. Using multiple ArcFace heads, we will simultaneously learn to place representations to optimize the leaf label as well as intermediate labels on the path from leaf to root of the label tree. Using taxonomically classified plankton image data, we will measure performance as a function of ArcFace parameters (sharpness/temperature and margins -- class-wise or level-wise), and compare the results to existing methods.

Advisor: Ketil Malde ( [email protected] )

Self-supervised object detection in video

One challenge with learning object detection is that in many scenes that stretch off into the distance, annotating small, far-off, or blurred objects is difficult. It is therefore desirable to learn from incompletely annotated scenes, and one-shot object detectors may suffer from incompletely annotated training data.

To address this, we will use a region-propsal algorithm (e.g. SelectiveSearch) to extract potential crops from each frame. Classification will be based on two approaches: a) training based on annotated fish vs random similarly-sized crops without annotations, and b) using a self-supervised method to build a representation for crops, and building a classifier for the extracted regions. The method will be evaluated against one-shot detectors and other training regimes.

If successful, the method will be applied to fish detection and tracking in videos from baited and unbaited underwater traps, and used to estimate abundance of various fish species.

See also: Benettino (2016): https://link.springer.com/chapter/10.1007/978-3-319-48881-3_56

Representation learning for object detection

While traditional classifiers work well with data that is labeled with disjoint classes and reasonably balanced class abundances, reality is often less clean. An alternative is to learn a vectors space embedding that reflects semantic relationships between objects, and deriving classes from this representation. This is especially useful for few-shot classification (ie. very few examples in the training data).

The task here is to extend a modern object detector (e.g. Yolo v8) to output an embedding of the identified object. Instead of a softmax classifier, we can learn the embedding either in a supervised manner (using annotations on frames) by attaching an ArcFace or other supervised metric learning head. Alternatively, the representation can be learned from tracked detections over time using e.g. a contrastive loss function to keep the representation for an object (approximately) constant over time. The performance of the resulting object detector will be measured on underwater videos, targeting species detection and/or indiviual recognition (re-ID).

Time-domain object detection

Object detectors for video are normally trained on still frames, but it is evident (from human experience) that using time domain information is more effective. I.e., it can be hard to identify far-off or occluded objects in still images, but movement in time often reveals them.

Here we will extend a state of the art object detector (e.g. yolo v8) with time domain data. Instead of using a single frame as input, the model will be modified to take a set of frames surrounding the annotated frame as input. Performance will be compared to using single-frame detection.

Large-scale visualization of acoustic data

The Institute of Marine Research has decades of acoustic data collected in various surveys. These data are in the process of being converted to data formats that can be processed and analyzed more easily using packages like Xarray and Dask.

The objective is to make these data more accessible to regular users by providing a visual front end. The user should be able to quickly zoom in and out, perform selection, export subsets, apply various filters and classifiers, and overlay annotations and other relevant auxiliary data.

Learning acoustic target classification from simulation

Broadband echosounders emit a complex signal that spans a large frequency band. Different targets will reflect, absorb, and generate resonance at different amplitudes and frequencies, and it is therefore possible to classify targets at much higher resolution and accuracy than before. Due to the complexity of the received signals, deriving effective profiles that can be used to identify targets is difficult.

Here we will use simulated frequency spectra from geometric objects with various shapes, orientation, and other properties. We will train ML models to estimate (recover) the geometric and material properties of objects based on these spectra. The resulting model will be applied to read broadband data, and compared to traditional classification methods.

Online learning in real-time systems

Build a model for the drilling process by using the Virtual simulator OpenLab ( https://openlab.app/ ) for real-time data generation and online learning techniques. The student will also do a short survey of existing online learning techniques and learn how to cope with errors and delays in the data.

Advisor: Rodica Mihai

Building a finite state automaton for the drilling process by using queries and counterexamples

Datasets will be generated by using the Virtual simulator OpenLab ( https://openlab.app/ ). The student will study the datasets and decide upon a good setting to extract a finite state automaton for the drilling process. The student will also do a short survey of existing techniques for extracting finite state automata from process data. We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin's L*algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.arxiv.org

Scaling Laws for Language Models in Generative AI

Large Language Models (LLM) power today's most prominent language technologies in Generative AI like ChatGPT, which, in turn, are changing the way that people access information and solve tasks of many kinds.

A recent interest on scaling laws for LLMs has shown trends on understanding how well they perform in terms of factors like the how much training data is used, how powerful the models are, or how much computational cost is allocated. (See, for example, Kaplan et al. - "Scaling Laws for Neural Language Models”, 2020.)

In this project, the task will consider to study scaling laws for different language models and with respect with one or multiple modeling factors.

Advisor: Dario Garigliotti

Applications of causal inference methods to omics data

Many hard problems in machine learning are directly linked to causality [1]. The graphical causal inference framework developed by Judea Pearl can be traced back to pioneering work by Sewall Wright on path analysis in genetics and has inspired research in artificial intelligence (AI) [1].

The Michoel group has developed the open-source tool Findr [2] which provides efficient implementations of mediation and instrumental variable methods for applications to large sets of omics data (genomics, transcriptomics, etc.). Findr works well on a recent data set for yeast [3].

We encourage students to explore promising connections between the fiels of causal inference and machine learning. Feel free to contact us to discuss projects related to causal inference. Possible topics include: a) improving methods based on structural causal models, b) evaluating causal inference methods on data for model organisms, c) comparing methods based on causal models and neural network approaches.

References:

1. Schölkopf B, Causality for Machine Learning, arXiv (2019):  https://arxiv.org/abs/1911.10500

2. Wang L and Michoel T. Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data. PLoS Computational Biology 13:e1005703 (2017).  https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005703

3. Ludl A and and Michoel T. Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast. arXiv:2010.07417  https://arxiv.org/abs/2010.07417

Advisors: Adriaan Ludl ,  Tom Michoel

Space-Time Linkage of Fish Distribution to Environmental Conditions

Conditions in the marine environment, such as, temperature and currents, influence the spatial distribution and migration patterns of marine species. Hence, understanding the link between environmental factors and fish behavior is crucial in predicting, e.g., how fish populations may respond to climate change.   Deriving this link is challenging because it requires analysis of two types of datasets (i) large environmental (currents, temperature) datasets that vary in space and time, and (ii) sparse and sporadic spatial observations of fish populations.

Project goal   

The primary goal of the project is to develop a methodology that helps predict how spatial distribution of two fish stocks (capelin and mackerel) change in response to variability in the physical marine environment (ocean currents and temperature).  The information can also be used to optimize data collection by minimizing time spent in spatial sampling of the populations.

The project will focus on the use of machine learning and/or causal inference algorithms.  As a first step, we use synthetic (fish and environmental) data from analytic models that couple the two data sources.  Because the ‘truth’ is known, we can judge the efficiency and error margins of the methodologies. We then apply the methodologies to real world (empirical) observations.

Advisors:  Tom Michoel , Sam Subbey . 

Towards precision medicine for cancer patient stratification

On average, a drug or a treatment is effective in only about half of patients who take it. This means patients need to try several until they find one that is effective at the cost of side effects associated with every treatment. The ultimate goal of precision medicine is to provide a treatment best suited for every individual. Sequencing technologies have now made genomics data available in abundance to be used towards this goal.

In this project we will specifically focus on cancer. Most cancer patients get a particular treatment based on the cancer type and the stage, though different individuals will react differently to a treatment. It is now well established that genetic mutations cause cancer growth and spreading and importantly, these mutations are different in individual patients. The aim of this project is use genomic data allow to better stratification of cancer patients, to predict the treatment most likely to work. Specifically, the project will use machine learning approach to integrate genomic data and build a classifier for stratification of cancer patients.

Advisor: Anagha Joshi

Unraveling gene regulation from single cell data

Multi-cellularity is achieved by precise control of gene expression during development and differentiation and aberrations of this process leads to disease. A key regulatory process in gene regulation is at the transcriptional level where epigenetic and transcriptional regulators control the spatial and temporal expression of the target genes in response to environmental, developmental, and physiological cues obtained from a signalling cascade. The rapid advances in sequencing technology has now made it feasible to study this process by understanding the genomewide patterns of diverse epigenetic and transcription factors as well as at a single cell level.

Single cell RNA sequencing is highly important, particularly in cancer as it allows exploration of heterogenous tumor sample, obstructing therapeutic targeting which leads to poor survival. Despite huge clinical relevance and potential, analysis of single cell RNA-seq data is challenging. In this project, we will develop strategies to infer gene regulatory networks using network inference approaches (both supervised and un-supervised). It will be primarily tested on the single cell datasets in the context of cancer.

Developing a Stress Granule Classifier

To carry out the multitude of functions 'expected' from a human cell, the cell employs a strategy of division of labour, whereby sub-cellular organelles carry out distinct functions. Thus we traditionally understand organelles as distinct units defined both functionally and physically with a distinct shape and size range. More recently a new class of organelles have been discovered that are assembled and dissolved on demand and are composed of liquid droplets or 'granules'. Granules show many properties characteristic of liquids, such as flow and wetting, but they can also assume many shapes and indeed also fluctuate in shape. One such liquid organelle is a stress granule (SG). 

Stress granules are pro-survival organelles that assemble in response to cellular stress and important in cancer and neurodegenerative diseases like Alzheimer's. They are liquid or gel-like and can assume varying sizes and shapes depending on their cellular composition. 

In a given experiment we are able to image the entire cell over a time series of 1000 frames; from which we extract a rough estimation of the size and shape of each granule. Our current method is susceptible to noise and a granule may be falsely rejected if the boundary is drawn poorly in a small majority of frames. Ideally, we would also like to identify potentially interesting features, such as voids, in the accepted granules.

We are interested in applying a machine learning approach to develop a descriptor for a 'classic' granule and furthermore classify them into different functional groups based on disease status of the cell. This method would be applied across thousands of granules imaged from control and disease cells. We are a multi-disciplinary group consisting of biologists, computational scientists and physicists. 

Advisors: Sushma Grellscheid , Carl Jones

Machine Learning based Hyperheuristic algorithm

Develop a Machine Learning based Hyper-heuristic algorithm to solve a pickup and delivery problem. A hyper-heuristic is a heuristics that choose heuristics automatically. Hyper-heuristic seeks to automate the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems [Handbook of Metaheuristics]. There might be multiple heuristics for solving a problem. Heuristics have their own strength and weakness. In this project, we want to use machine-learning techniques to learn the strength and weakness of each heuristic while we are using them in an iterative search for finding high quality solutions and then use them intelligently for the rest of the search. Once a new information is gathered during the search the hyper-heuristic algorithm automatically adjusts the heuristics.

Advisor: Ahmad Hemmati

Machine learning for solving satisfiability problems and applications in cryptanalysis

Advisor: Igor Semaev

Hybrid modeling approaches for well drilling with Sintef

Several topics are available.

"Flow models" are first-principles models simulating the flow, temperature and pressure in a well being drilled. Our project is exploring "hybrid approaches" where these models are combined with machine learning models that either learn from time series data from flow model runs or from real-world measurements during drilling. The goal is to better detect drilling problems such as hole cleaning, make more accurate predictions and correctly learn from and interpret real-word data.

The "surrogate model" refers to  a ML model which learns to mimic the flow model by learning from the model inputs and outputs. Use cases for surrogate models include model predictions where speed is favoured over accuracy and exploration of parameter space.

Surrogate models with active Learning

While it is possible to produce a nearly unlimited amount of training data by running the flow model, the surrogate model may still perform poorly if it lacks training data in the part of the parameter space it operates in or if it "forgets" areas of the parameter space by being fed too much data from a narrow range of parameters.

The goal of this thesis is to build a surrogate model (with any architecture) for some restricted parameter range and implement an active learning approach where the ML requests more model runs from the flow model in the parts of the parameter space where it is needed the most. The end result should be a surrogate model that is quick and performs acceptably well over the whole defined parameter range.

Surrogate models trained via adversarial learning

How best to train surrogate models from runs of the flow model is an open question. This master thesis would use the adversarial learning approach to build a surrogate model which to its "adversary" becomes indistinguishable from the output of an actual flow model run.

GPU-based Surrogate models for parameter search

While CPU speed largely stalled 20 years ago in terms of working frequency on single cores, multi-core CPUs and especially GPUs took off and delivered increases in computational power by parallelizing computations.

Modern machine learning such as deep learning takes advantage this boom in computing power by running on GPUs.

The SINTEF flow models in contrast, are software programs that runs on a CPU and does not happen to utilize multi-core CPU functionality. The model runs advance time-step by time-step and each time step relies on the results from the previous time step. The flow models are therefore fundamentally sequential and not well suited to massive parallelization.

It is however of interest to run different model runs in parallel, to explore parameter spaces. The use cases for this includes model calibration, problem detection and hypothesis generation and testing.

The task of this thesis is to implement an ML-based surrogate model in such a way that many surrogate model outputs can be produced at the same time using a single GPU. This will likely entail some trade off with model size and maybe some coding tricks.

Uncertainty estimates of hybrid predictions (Lots of room for creativity, might need to steer it more, needs good background literature)

When using predictions from a ML model trained on time series data, it is useful to know if it's accurate or should be trusted. The student is challenged to develop hybrid approaches that incorporates estimates of uncertainty. Components could include reporting variance from ML ensembles trained on a diversity of time series data, implementation of conformal predictions, analysis of training data parameter ranges vs current input, etc. The output should be a "traffic light signal" roughly indicating the accuracy of the predictions.

Transfer learning approaches

We're assuming an ML model is to be used for time series prediction

It is possible to train an ML on a wide range of scenarios in the flow models, but we expect that to perform well, the model also needs to see model runs representative of the type of well and drilling operation it will be used in. In this thesis the student implements a transfer learning approach, where the model is trained on general model runs and fine-tuned on a most representative data set.

(Bonus1: implementing one-shot learning, Bonus2: Using real-world data in the fine-tuning stage)

ML capable of reframing situations

When a human oversees an operation like well drilling, she has a mental model of the situation and new data such as pressure readings from the well is interpreted in light of this model. This is referred to as "framing" and is the normal mode of work. However, when a problem occurs, it becomes harder to reconcile the data with the mental model. The human then goes into "reframing", building a new mental model that includes the ongoing problem. This can be seen as a process of hypothesis generation and testing.

A computer model however, lacks re-framing. A flow model will keep making predictions under the assumption of no problems and a separate alarm system will use the deviation between the model predictions and reality to raise an alarm. This is in a sense how all alarm systems work, but it means that the human must discard the computer model as a tool at the same time as she's handling a crisis.

The student is given access to a flow model and a surrogate model which can learn from model runs both with and without hole cleaning and is challenged to develop a hybrid approach where the ML+flow model continuously performs hypothesis generation and testing and is able to "switch" into predictions of  a hole cleaning problem and different remediations of this.

Advisor: Philippe Nivlet at Sintef together with advisor from UiB

Explainable AI at Equinor

In the project Machine Teaching for XAI (see  https://xai.w.uib.no ) a master thesis in collaboration between UiB and Equinor.

Advisor: One of Pekka Parviainen/Jan Arne Telle/Emmanuel Arrighi + Bjarte Johansen from Equinor.

Explainable AI at Eviny

In the project Machine Teaching for XAI (see  https://xai.w.uib.no ) a master thesis in collaboration between UiB and Eviny.

Advisor: One of Pekka Parviainen/Jan Arne Telle/Emmanuel Arrighi + Kristian Flikka from Eviny.

If you want to suggest your own topic, please contact Pekka Parviainen ,  Fabio Massimo Zennaro or Nello Blaser .

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

Machine Learning Project Topics With Abstracts and Base Papers 2024

Embark on a journey into the realm of machine learning with our curated list of M.Tech project topics for 2024, complemented by trending IEEE base papers. These projects cover a spectrum of innovative applications and advancements in the field, offering an invaluable resource for M.Tech students seeking to push the boundaries of knowledge and skill. Our comprehensive collection encompasses diverse Machine Learning project topics, each accompanied by a meticulously selected base paper and a concise abstract. From natural language processing and computer vision to reinforcement learning and predictive analytics, these projects reflect the latest trends in the ever-evolving landscape of artificial intelligence. Stay ahead of the curve by exploring projects that align with the current demands and challenges faced by industries worldwide. Whether you are a student, researcher, or industry professional, our compilation serves as a gateway to the forefront of machine learning innovation. The project titles are strategically chosen to include relevant keywords, ensuring alignment with the latest IEEE standards and technological advancements. Dive into the abstracts to gain a quick insight into the scope, methodology, and potential impact of each project.

M.Tech Projects Topics List In Machine Learning

Shivam Kashyap

Cloud computing project topics with abstracts and base papers 2024, computer network project topics with abstracts and base papers 2024, you may also like.

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The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an  introduction , which presents a brief overview of the topic and the  research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging:  A deep learning approach to improve the accuracy of medical diagnoses.

Introduction:  Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction:  Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction:  Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction:  Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction:  Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by  Joanna Kosinska  on  Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction:  Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction:  Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction:  Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction:  Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction:  Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

Photo by  UX Indonesia  on  Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction:  Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction:  Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction:  Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction:  Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction:  Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

Photo by  Windows  on  Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction:  Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction:  Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction:  Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction:  Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction:  Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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Dissertations / Theses on the topic 'Machine Learning (ML)'

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Holmberg, Lars. "Human In Command Machine Learning." Licentiate thesis, Malmö universitet, Malmö högskola, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42576.

Nangalia, V. "ML-EWS - Machine Learning Early Warning System : the application of machine learning to predict in-hospital patient deterioration." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1565193/.

John, Meenu Mary. "Design Methods and Processes for ML/DL models." Licentiate thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-45026.

Tabell, Johnsson Marco, and Ala Jafar. "Efficiency Comparison Between Curriculum Reinforcement Learning & Reinforcement Learning Using ML-Agents." Thesis, Blekinge Tekniska Högskola, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20218.

Mattsson, Fredrik, and Anton Gustafsson. "Optimize Ranking System With Machine Learning." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-37431.

Kakadost, Naser, and Charif Ramadan. "Empirisk undersökning av ML strategier vid prediktion av cykelflöden baserad på cykeldata och veckodagar." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20168.

Sammaritani, Gloria. "Google BigQuery ML. Analisi comparativa di un nuovo framework per il Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

Gustafsson, Sebastian. "Interpretable serious event forecasting using machine learning and SHAP." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444363.

Schoenfeld, Brandon J. "Metalearning by Exploiting Granular Machine Learning Pipeline Metadata." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8730.

Hellborg, Per. "Optimering av datamängder med Machine learning : En studie om Machine learning och Internet of Things." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13747.

Nämerforslund, Tim. "Machine Learning Adversaries in Video Games : Using reinforcement learning in the Unity Engine to create compelling enemy characters." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42746.

Mahfouz, Tarek Said. "Construction legal support for differing site conditions (DSC) through statistical modeling and machine learning (ML)." [Ames, Iowa : Iowa State University], 2009.

REPETTO, MARCO. "Black-box supervised learning and empirical assessment: new perspectives in credit risk modeling." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/402366.

Björkberg, David. "Comparison of cumulative reward withone, two and three layered artificialneural network in a simple environmentwhen using ml-agents." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21188.

Ucci, Graziano. "The Interstellar Medium of Galaxies: a Machine Learning Approach." Doctoral thesis, Scuola Normale Superiore, 2019. http://hdl.handle.net/11384/85928.

Gilmore, Eugene M. "Learning Interpretable Decision Tree Classifiers with Human in the Loop Learning and Parallel Coordinates." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/418633.

Sridhar, Sabarish. "SELECTION OF FEATURES FOR ML BASED COMMANDING OF AUTONOMOUS VEHICLES." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287450.

Lagerkvist, Love. "Neural Novelty — How Machine Learning Does Interactive Generative Literature." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21222.

Bhogi, Keerthana. "Two New Applications of Tensors to Machine Learning for Wireless Communications." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104970.

Garg, Anushka. "Comparing Machine Learning Algorithms and Feature Selection Techniques to Predict Undesired Behavior in Business Processesand Study of Auto ML Frameworks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285559.

Hanski, Jari, and Kaan Baris Biçak. "An Evaluation of the Unity Machine Learning Agents Toolkit in Dense and Sparse Reward Video Game Environments." Thesis, Uppsala universitet, Institutionen för speldesign, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444982.

Stellmar, Justin. "Predicting the Deformation of 3D Printed ABS Plastic Using Machine Learning Regressions." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1587462911261523.

Krüger, Franz David, and Mohamad Nabeel. "Hyperparameter Tuning Using Genetic Algorithms : A study of genetic algorithms impact and performance for optimization of ML algorithms." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42404.

Björkman, Desireé. "Machine Learning Evaluation of Natural Language to Computational Thinking : On the possibilities of coding without syntax." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424269.

Lundin, Lowe. "Artificial Intelligence for Data Center Power Consumption Optimisation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447627.

Wessman, Filip. "Advanced Algorithms for Classification and Anomaly Detection on Log File Data : Comparative study of different Machine Learning Approaches." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43175.

Narmack, Kirilll. "Dynamic Speed Adaptation for Curves using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233545.

Mathias, Berggren, and Sonesson Daniel. "Design Optimization in Gas Turbines using Machine Learning : A study performed for Siemens Energy AB." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173920.

Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

Tarullo, Viviana. "Artificial Neural Networks for classification of EMG data in hand myoelectric control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19195/.

Wallner, Vanja. "Mapping medical expressions to MedDRA using Natural Language Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426916.

Hellberg, Johan, and Kasper Johansson. "Building Models for Prediction and Forecasting of Service Quality." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295617.

Nardello, Matteo. "Low-Power Smart Devices for the IoT Revolution." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/274371.

Nardello, Matteo. "Low-Power Smart Devices for the IoT Revolution." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/274371.

Michelini, Mattia. "Barcode detection by neural networks on Android mobile platforms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21080/.

Lundström, Robin. "Machine Learning for Air Flow Characterization : An application of Theory-Guided Data Science for Air Fow characterization in an Industrial Foundry." Thesis, Karlstads universitet, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72782.

Daneshvar, Saman. "User Modeling in Social Media: Gender and Age Detection." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39535.

Rosell, Felicia. "Tracking a ball during bounce and roll using recurrent neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239733.

Hallberg, Jesper. "Searching for the charged Higgs boson in the tau nu analysis using Boosted Decision Trees." Thesis, Uppsala universitet, Högenergifysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301351.

Klingvall, Emelie. "Artificiell intelligens som ett beslutsstöd inom mammografi : En kvalitativ studie om radiologers perspektiv på icke-tekniska utmaningar." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18768.

Alatram, Ala'a A. M. "A forensic framework for detecting denial-of-service attacks in IoT networks using the MQTT protocol." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2561.

Forssell, Melker, and Gustav Janér. "Product Matching Using Image Similarity." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413481.

Bengtsson, Theodor, and Jonas Hägerlöf. "Stora mängder användardata för produktutveckling : Möjligheter och utmaningar vid integrering av stora mängder användardata i produktutvecklingsprocesser." Thesis, KTH, Integrerad produktutveckling, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297966.

Nordqvist, My. "Classify part of day and snow on the load of timber stacks : A comparative study between partitional clustering and competitive learning." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42238.

Mele, Matteo. "Convolutional Neural Networks for the Classification of Olive Oil Geographical Origin." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

Hjerpe, Adam. "Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496.

Ahlm, Kristoffer. "IDENTIFIKATION AV RISKINDIKATORER I FINANSIELL INFORMATION MED HJÄLP AV AI/ML : Ökade möjligheter för myndigheter att förebygga ekonomisk brottslighet." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184818.

Zanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.

Benatti, Mattia. "Progettazione e Sviluppo di una Piattaforma Multi-Sorgente per l’Ottimizzazione dei Servizi di Emergenza." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

Sibelius, Parmbäck Sebastian. "HMMs and LSTMs for On-line Gesture Recognition on the Stylaero Board : Evaluating and Comparing Two Methods." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162237.

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M.Tech. Artificial Intelligence and Machine Learning

With a surge of job opportunities in the fields of Artificial intelligence and Machine Learning, the world is indeed standing on the threshold of massive transformation.

According to the World Economic Forum's Future of Jobs Report, 85 million jobs will be replaced by machines with AI by 2025. While that might make you uneasy, the same report states that 97 million new jobs will be created by 2025 due to AI. Are you prepared? 

Prepare for a career with infinite possibilities in AI and ML with India’s most comprehensive and world-class M.Tech. Artificial Intelligence and Machine Learning programme without taking any career break.

This four-semester programme covers a wide variety of skills and knowledge areas, and enables IT professionals and Software developers to build a skill set that enables career elevation in some of the most sought-after job roles such as ML Engineers and AI Scientists, etc.

Option to pay fees using easy EMI with 0% interest and 0 down payment.

WILP programs are

UGC approved

Easy EMI Option With

0% Interest & 0 down payment

Admission begins

August 2024

Fees per semester

Programme Highlights

• M.Tech. Artificial Intelligence and Machine Learning is a BITS Pilani Work Integrated Learning Programme (WILP). BITS Pilani Work Integrated Learning Programmes are UGC approved.
• This programme is of 4 semesters and can be pursued only by working professionals. You can pursue the programme without any career break.
• The programme will also enable working professionals to attend contact classes from anywhere over a technology-enabled platform. The contact classes will be conducted mostly on weekends or after business hours.
• Offers the most comprehensive AI & ML Curriculum for working professionals.
• The programme has an unmatched range & depth, and covers the widest variety of skill & knowledge areas required to develop advanced AI solutions.
• Meant for IT professionals and Software developers aiming to become expert Machine Learning Engineers & AI Scientists.
• The programme offers a set of core courses and elective courses, allowing students to gain expertise in Advanced Deep learning, Natural Language Processing, etc.
• The programme makes use of Tools and Technologies. These include Tensorflow for Deep Learning and various Python libraries for data processing, machine learning, OpenCV for computer vision, NLTK for NLP etc.
• The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learned during the programme.
• The programme uses a Continuous Evaluation System that assesses the learners over convenient and regular intervals. Such a system provides timely and frequent feedback and helps busy working professionals stay on course with the programme.
• The education delivery methodology is a blend of classroom and experiential learning. Experiential learning consists of Virtual lab exercises, assignments, case studies and work-integrated activities.
• Participants who successfully complete the programme will become members of an elite & global community of BITS Pilani Alumni
• Option to submit fee using easy-EMI with 0% interest and 0 down payment .

UGC Approval

BITS Pilani is an Institution of Eminence under UGC (Institution of Eminence Deemed to be Universities) Regulations, 2017. The Work Integrated Learning Programmes (WILP) of BITS Pilani constitutes a unique set of educational offerings for working professionals. WILP are an extension of programmes offered at the BITS Pilani Campuses and are comparable to our regular programmes both in terms of unit/credit requirements as well as academic rigour. In addition, it capitalises and further builds on practical experience of students through high degree of integration, which results not only in upgradation of knowledge, but also in up skilling, and productivity increase. The programme may lead to award of degree, diploma, and certificate in science, technology/engineering, management, and humanities and social sciences. On the recommendation of the Empowered Expert Committee, UGC in its 548th Meeting held on 09.09.2020 has approved the continued offering of BITS Pilani’s Work Integrated Learning programmes.

Programme Curriculum

Participants need to take at least 12 courses towards coursework, and complete one Project/ Dissertation. The coursework requirement for the programme would consist of a set of core courses and electives. Core courses are compulsory for all participants, while electives can be chosen based on individual learning preferences.

• First Semester
• Second Semester
• Third Semester
• Fourth Semester
• Mathematical Foundations for Machine Learning.
• Machine Learning
• Introduction to Statistical Methods
• Artificial and Computational Intelligence
• Deep Neural Networks
• Deep Reinforcement Learning
• Dissertation

Pool of Electives for : Deep Learning Specialization

• Advanced Deep learning #
• Graph Neural Networks
• ML System Optimization
• Fair, Accountable, Transparent Machine Learning

Note: 3 courses are required including the course marked in #

Pool of Electives for : NLP Specialization

• NLP Applications
• Social Media Analytics
• Natural Language Processing
• Information Retrieval
• Conversational AI

General Pool of Electives

• Design of Algorithms
• Computer Vision
• Probabilistic Graphical Models
• Data Management for Machine Learning
• Video Analytics
• Advanced Data Mining
• AI and ML techniques for Cyber Security

Note: Choice of Electives is made available to enrolled students at the beginning of each semester. Students' choice will be taken as one of the factors while deciding on the Electives offered. However, Electives finally offered will be at the discretion of the Institute

For detailed programme curriculum, download the brochure.

Mode of learning.

The Mode of Learning used in this programme is called - Work Integrated Learning. Internationally, Work Integrated Learning (WIL) is defined as "An educational approach involving three parties - the student, educational institution, and employer organization(s) - consisting of authentic work-focused experiences as an intentional component of the curriculum. Students learn through active engagement in purposeful work tasks, which enable the integration of theory with meaningful practice that is relevant to the students' discipline of study and/or professional development*.

An education model can be considered as WIL if and only if:

• The programs are designed and developed by the institute in collaboration with industry.
• Work-focused experiences form an active part of the curriculum.
• The program structure, pedagogy and assessment enable integration of theory-with relevant practice.

The innovative Work Integrated Learning Programs (WILP) of BITS Pilani are quite aligned with the above definition and requirements. The programs are designed in collaboration with its industry partners, subject matter experts from industry and academia that enable the students to remain relevant in their chosen profession, grow in their career and retain the habit of lifelong learning. The continued availability of workplace related experiences along with the weekly instruction sessions promote integration of theory with practice. An active participation of the organization mentor in the learning process of the student plays a key role. Case studies, simulation exercises, labs and projects further strengthen this integration.

The WILP of BITS Pilani is comparable to its campus-based programs in terms of structure, rigor, instruction, labs, assessment, faculty profile and learning support. The pervasive adoption of technology in all its academic processes makes the same high-quality education of BITS Pilani available to the aspirants at scale with the required flexibility. 

Key Benefits of BITS Pilani WILP

1) Can pursue the programme without any career break and along with the job.

2) The programme curriculum is highly relevant to sectors, industries and organisations they work for

3) In addition to the institute, the learning experience of working professionals in the programme is also supported by the employer organisation and Industry Mentors.

4) Effective use of technology to deliver a range of learning interventions at the location of the working professional such as faculty contact sessions, asynchronous learning materials, remote, virtual and cloud labs, Learner support, peer to peer collaboration etc. 

5) Contact sessions with faculty take place mostly over weekends or after business hours and are conducted over a technology platform that can be accessed from anywhere. 

6) Mid semester and End semester examinations for every semester are conducted mostly at designated examination centres distributed across the country (for details refer to link mode of examinations) 

7) Learners can access engaging learning material which includes recorded lectures from BITS Pilani faculty members, course handouts and recorded lab content where applicable.

EXPERIENTIAL LEARNING

The programme emphasises on Experiential Learning that allows learners to apply concepts learnt in the classroom in simulated, and real work situations. This is achieved through:

• Simulation Tools, Platforms & Environments: Some or all of the following would be utilised across the programme. Tensorflow for Deep Learning and various Python libraries for data processing, machine learning, OpenCV for computer vision, NLTK for NLP etc.

CONTINUOUS ASSESSMENT

Continuous Assessment includes graded Assignments/ Quizzes, Mid-semester exam, and Comprehensive Exam.

CASE STUDIES AND ASSIGNMENTS

Carefully chosen real-world cases & assignments are both discussed and used as problem-solving exercises during the programme.

DISSERTATION/ PROJECT WORK

The fourth semester offers an opportunity for learners to apply their knowledge gained during the programme to a real-world like complex project. The learner is expected to demonstrate understanding of vital principles learnt across semesters and their ability to successfully apply these concepts.

Industry Talks

The programme features Industry Talks on some of the most exciting developments, and pressing issues faced by businesses in the technology space. Speakers include business leaders, R&D professionals, and academicians from leading technology firms and institutions. 

Some of the recent and upcoming sessions include:

Mode Of Examination

Mode of Examinations applicable for students admitted in Batch starting in April/May 2024:

Semester 1, 2 and 3 have Mid-Semester Examinations and Comprehensive Examinations for each course. These examinations are mostly scheduled on Friday, Saturday or Sunday. Students need to  appear in person for taking the examinations at the institution’s designated examination centres  as per the examination schedule, Instructions, rules and guidelines announced before every examination. 

Students can take their examination at any of our  23 designated examination centres in India  at the following locations:

South Zone:  Bangalore, Chennai, Hyderabad, Vijayawada, Visakhapatnam, Kochi, Thiruvananthapuram and Coimbatore. 

North Zone:  Delhi NCR, Jaipur, Chandigarh, Lucknow and Pilani. 

West Zone : Mumbai, Pune, Goa, Ahmedabad, Indore and Nagpur.

East Zone:  Kolkata, Bhubaneshwar, Guwahati and Jamshedpur.

In addition to these locations, the Institution also has a designated examination centre in  Dubai.

During these semesters, in addition to the above mentioned Mid-Semester and Comprehensive examinations, there will also be Quizzes/Assignments conducted online on the Learning Management System (LMS) as per the course plan in which the students need to participate.

In Semester 4 (Final Semester), the student will be doing Dissertation/Project Work as per the Institution’s guidelines.

Eligibility Criteria

• Employed professionals holding B.E. / B.Tech. with at least 60% aggregate marks and minimum one-year relevant work experience after the completion of the degree are eligible to apply.
• Employed professionals holding MCA / M.Sc. or equivalent with at least 60% aggregate marks with university level mathematics / statistics as mandatory subjects and minimum one-year relevant work experience after the completion of the degree are also eligible to apply.
• Working knowledge of Computing and programming is required.

Fee Structure

The following fees schedule is applicable for candidates seeking new admission during the academic year 2023-2024.

• Application Fees (one time) : INR 1,500
• Admission Fees (one time) : INR 16,500
• Semester Fees (per semester) : INR 68,500

Fee Payment Schedule:

• Block Amount: INR 25,000 (payable within 7 days of receipt of provisional Admission Offer Letter
• Remainder Programme Fee: INR 2,00,000 (payable within 15 days of receipt of Final Admission Offer Letter)
• Pay fee in easy EMIs of INR 13,333 with 0% interest. Click here to know more.

The one-time Application Fee is to be paid at the time of submitting the Application Form through the Online Application Centre.

Admission Fee (one-time) and Semester Fee (for the First Semester) are to be paid together once admission is offered to the candidate. Thus, a candidate who has been offered admission will have to pay Rs. 85,000/-. You may choose to make the payment using Netbanking/ Debit Card/ Credit Card through the Online Application Centre. Fee payment by easy-EMIs with 0% interest. Click here to learn more.

Semester Fee for subsequent semesters will only be payable later, i.e. at the beginning of those respective semesters.

Any candidate who desires to discontinue from the programme after confirmation of admission & registration for the courses specified in the admit offer letter will forfeit the total amount of fees paid.

For the examination centre at Dubai, in addition to the semester fees, for each semester there will be an examination centre fees of 1000 UAE Dirhams or equivalent per semester out of which 500 UAE Dirhams is to be paid at the time of appearing in Mid-semester examinations at Dubai Centre for that semester and the remaining 500 UAE Dirhams is to be paid at the time of appearing in comprehensive examinations at Dubai centre for that semester.

All the above fees are non-refundable.

Important : For every course in the program institute will recommend textbooks, students would need to procure these textbooks on their own.

How to Apply

• Click here to visit the BITS Pilani Online Application Center. Create your login at the Application Center by entering your unique Email id and create a password of your choice.
• Once your login has been created, you can anytime access the Online Application Center using your email id and password. Once you have logged in, you will see a screen showing 4 essential steps to be completed to apply for the programme of your choice.
• Begin by clicking on Step 1 - ‘Fill/ Edit and Submit Application Form’. This will enable you to select the programme of your choice. After you have chosen your programme, you will be asked to fill your details in an online form. You must fill all the details and press ‘Submit’ button given at the bottom of the form.
• Take the next step by clicking on Step 2 - 'Download Application PDF Copy’. This will download a pdf copy of the application form on your computer.
• Now, click on Step 3 - 'Pay Application Fee’ to pay INR 1,500/- using Netbanking/ Debit Card/ Credit Card.
• Take a printout of the downloaded Application Form and note down the Application Form Number displayed on the top-right corner of the first page. This Application Form Number should be referred in all future correspondence with BITS Pilani.

In the printout of the downloaded Application Form, you will notice on page no. 3 a section called the Employer Consent Form. Complete the Employer Consent Form. This form needs to be signed and stamped by your organisation’s HR or any other authorised signatory of the company.

Important : In view of work-from-home policies mandated by many organisations, a few candidates may not be able to get the physical forms signed by their HR/ other authorised organisational representative. Such candidates may instead request an email approval to be sent to their official email ID by the HR using the format available through this link .

Further on page no. 4 of the printed Application Form is a section called the Mentor Consent Form. The Mentor Consent Form needs to be signed by the Mentor. Click here to know who could be a Mentor.

Who is a Mentor:

Candidates applying to Work Integrated Learning Programmes must choose a Mentor, who will monitor the academic progress of the candidate, and act as an advisor & coach for successful completion of the programme. Candidates should ideally choose the immediate supervisor or another senior person from the same organisation. In case a suitable mentor is not available in the same organisation, a candidate could approach a senior person in another organisation who has the required qualifications. Wherever the proposed Mentor is not from the same employing organization as that of the candidate, a supporting document giving justification for the same should be provided by the candidate’s employer.

Candidates applying to B.Tech. programmes should choose a Mentor who is an employed professional with B.E./ B.S./ B.Tech./ M.Sc./ A.M.I.E./ Integrated First Degree of BITS or equivalent

Candidates applying to M.Tech., M.Sc., MBA, M.Phil programme should choose a Mentor who is an employedprofessional with:

B.E. / M.Sc. / M.B.A. / M.C.A. / M.B.B.S. etc. and with a minimum of five years of relevant work experience

M.E./ M.S./ M.Tech./ M.Phil./ M.D./ Higher Degree of BITS or equivalent

Important : In view of work-from-home policies mandated by many organisations, a few candidates may not be able to get the physical forms signed by their Mentor. Such candidates may instead request an email approval to be sent to their official email ID by the Mentor using the format available through this link .

• Further on page no. 5 of the downloaded Application Form, is a Checklist of Enclosures/ Attachments.
• Make photocopies of the documents mentioned in this Checklist.
• Applicants are required to self-attest all academic mark sheets and certificates.
• Finally, click on Step 4 - 'Upload & Submit All Required Documents’. This will allow you to upload one-by-one the printed Application Form, Mentor Consent Form, Employer Consent Form, and all mandatory supporting documents and complete the application process. Acceptable file formats for uploading these documents are DOC, DOCX, PDF, ZIP and JPEG.
• Upon receipt of your Application Form and all other enclosures, the Admissions Cell will scrutinise them for completeness, accuracy and eligibility.
• Admission Cell will intimate selected candidates by email within two weeks of submission of application with all supporting documents. The selection status can also be checked by logging in to the Online Application Centre.

Note: It is also possible that some candidates may receive a link to take an Online Learning Readiness Evaluation from the Admission Cell, after filling up the Online Application Form. This is a one-hour objective-type exercise which will ascertain minimum mathematical and programming acumen needed to pursue the programme. You will be given typically 48-hours to complete this online evaluation.

A sample model paper will also be provided to help you understand the format of Online Learning Readiness Evaluation. After the Online Learning Readiness Evaluation is completed, the Admission Cell will intimate selected candidates by email within one week.

Batch Profile

1. How are classes conducted?

Contact classes are conducted over a technology enabled platform. These classes can be attended via the internet using a computer from any location. These contact classes offer similar levels of interactivity as regular classrooms at the BITS Pilani campus. These are conducted usually over weekends or after business hours for a total of 7-8 hours per week. If you miss a lecture, you can also access the recorded lecture on the internet.

2. Who can apply to this programme?

This programme is designed for working professionals. At the time of submitting the application, candidates must be employed in another organization. Professionals who are owners of a registered business are also eligible to apply. For detailed information, including academic background, work experience, etc. refer to the programme eligibility criteria.

3. What kind of certificate will I receive at the end of the programme?

Upon successful completion of the programme, participants will receive a degree certificate from BITS Pilani.

4. How will exams be conducted?

Each semester has a Mid-semester Exam and a Comprehensive Exam for each course  , which are conducted over weekends. Students will need to appear in-person to take these exams at exams centers in the following locations: India Centers:

South Zone: Bangalore, Chennai, Hyderabad, Vijayawada, Visakhapatnam, Kochi, Thiruvananthapuram and Coimbatore.

North Zone: Delhi NCR, Jaipur, Chandigadh, Lucknow and Pilani.

West Zone: Mumbai, Pune, Goa, Ahmedabad, Indore and Nagpur.

East Zone: Kolkata, Bhubaneshwar, Guwahati and Jamshedpur. International Centers: Dubai In case students are unable to take an exam due to work-related commitments, there is also a provision of appearing for Make-up Exams.  

5. Will I be able to interact with the faculty members?

The programme provides a high degree of interactivity between students and the faculty members and programme instructors. Q&A sessions during the contact classes (which you can attend from anywhere over a technology enabled platform) allow participants to pose questions to the faculty members, and seek guidance through voice and chat. Further interaction with faculty members and peers is enabled through the e-learning portal, by using discussion forums and message boards.

6. How will I be able to run experiments and access labs for this program?

The programme features high usage of experiential learn components such as Simulations, Virtual Labs, and Remote Labs, in order to mimic the on-campus experience.

Participants will be given access to portals, that will allow them to access both cloud-based labs, as well as Campus-based physical labs. Using leading industry-recognised Software tools, Programming languages, and Simulation software, participants will be able to perform experiments and run simulations to advance their knowledge

7. Who is a Mentor? Will BITS Pilani help me find a mentor?

Candidates applying to the programmes must choose a Mentor, who will monitor the academic progress of the candidate, and act as an advisor & coach for successful completion of the programme. Candidates should ideally choose the immediate supervisor or another senior person from the same organisation. In case a suitable mentor is not available in the same organisation, a candidate could approach a senior person in another organisation who has the required qualifications. For detailed information, please refer to the programme brochure.

8. Do I need to visit a BITS campus during the programme?

The programme is designed for working professionals, and participants are not required to travel to a BITS campus. However, certain programme offer Campus Immersion Modules such as workshops or seminars, which are highly recommended but not mandatory.

9. Is this programme approved by UGC?

Yes, UGC has approved the continued offering of M.Tech. Artificial Intelligence and Machine Learning as a part of WILP on the recommendations of Empowered Expert Committee. Click here to learn more.

10. What is M.Tech. in Artificial Intelligence and Machine Learning programme from BITS Pilani WILP?

It is a post-graduate programme that focuses on providing advanced knowledge and skills in the fields of artificial intelligence and machine learning. The programme covers topics such as machine learning, natural language processing, neural networks, computer vision, robotics, deep learning, data analysis and more.

11. What are the eligibility criteria for M.Tech. in Artificial Intelligence and Machine Learning programme?

Applicants for the M.Tech. in Artificial Intelligence and Machine Learning program must meet these criteria:

• Employed professionals with a B.E. / B.Tech. or equivalent degree, securing at least 60% aggregate marks
• Possessing a minimum of one year of relevant work experience post-degree completion, are eligible to apply.
• In addition, a working knowledge of computing and programming is required.
• Employed professionals with an MCA / M.Sc. or equivalent degree, achieving at least 60% aggregate marks,
• With mandatory subjects in mathematics or statistics at the university level,
• Possessing a minimum of one year of relevant work experience post-degree completion, are also eligible to apply.

12. What are the job prospects after completing M.Tech. in Artificial Intelligence and Machine Learning programme?

Graduates in M.Tech. in Artificial Intelligence and Machine Learning programme are currently in high demand due to the increasing adoption of AI and ML technologies across industries, such as IT, Retail, Automotive, Manufacturing, Finance, Healthcare, e-commerce, and Telecommunications. Some of the job roles that graduates can explore include AI engineer, ML engineer, Data scientist, Data analyst, and Research scientist.

13. What skills are required to pursue the M.Tech. in Artificial Intelligence and Machine Learning programme?

14. what is the duration of the m.tech. in artificial intelligence and machine learning programme, featured faculty.

Dr. G. Venkiteswaran

Dr. Venkiteswaran obtained his Ph. D in Industrial Mathematics from the Technical ...

Dr. Venkiteswaran obtained his Ph. D in Industrial Mathematics from the Technical University of Kaiserslautern (TUKL), Germany, in the year 2003. After doing a post-doc at the Graduate School in TUKL for close to 18 months, he joined BITS, Pilani as a visiting faculty in the Department of Mathematics, in 2004. Presently, he is the Associate Dean for Quality Assurance and Assessment in the Work Integrated Learning Programmes Division. With over 23 years of teaching experience, he teaches courses in Mathematics and Computer Science. His main research interests are in the area of numerical solution of partial differential equations in high dimensions, Monte Carlo and quasi-Monte Carlo methods.

Prof. Ankur Pachauri

Prof. Ankur Pachauri, is appointed as an Assistant Professor in the Department of ...

Prof. Ankur Pachauri, is appointed as an Assistant Professor in the Department of Computer Science and Information Science, in WILP, Off-Campus center, Pune, since August 2015 and has teaching experience of over 9 years. He had done his schooling from a missionary school, then higher secondary from Kendriya Vidyalaya in the year 2000. He is a Mathematics Honors Graduate of the year 2003, Masters of 2006 and completed doctorate from Department of Mathematics, Dayalbagh Educational Institute, Agra in January 2013. He had also worked as Assistant Professor from June 2012 till July 2015 in Rajiv Academy for Technology and Management, Mathura. He had taught various courses on Algorithms, and Applied Mathematics courses. He has published a number of research papers in various reputed Journals and National & International Conferences Proceedings and they are cited by a large number of Researchers, from various countries. His area of research is Nature Inspired Algorithms, Evolutionary Algorithm, Search Based Software Engineering, Soft Computing and Graph Mining

Prof. K. Gopala Krishna

Prof. K. Gopala Krishna is currently Associate Professor in BITS-Pilani WILP Divis...

Prof. K. Gopala Krishna is currently Associate Professor in BITS-Pilani WILP Division. He has been teaching working professionals in Indian IT organizations since 2009. Prior to his current position as faculty member, he has had over 35 years of experience in the IT industry in the design and delivery of products and services for global clients in Embedded Systems and Enterprise Software arena. He is passionate about Science and Electronics and in his spare time loves to dirty his hands building physical models and run thought- experiments on paper when run out of resources. His interests include Teaching-Learning, Design Thinking, Internet-of-Things, Developmental Economics, eGovernance and Digital Organizations. Krishna started his career with typing lines of Fortran code on a punch-card machine after graduating with M.Tech from Indian Institute of Science, Bangalore.

Prof. Mohammad Saleem Bagewadi

Prof. Mohammad Saleem Bagewadi is an Assistant Professor with the Compute Science ...

Prof. Mohammad Saleem Bagewadi is an Assistant Professor with the Compute Science and Information Systems group of WILP Division, BITS-Pilani. He did his M.Tech from Visvesvaraya Technological University (VTU) in Computer Networks Engineering in 2008. He worked as a QA Network Engineer for 7 years in Juniper Networks and has worked on HighEnd/Core Router network technologies, Layer 2 and Layer 3 protocols. He has been actively involved in industry engagement, content development and delivery of course content. He is very passionate about teaching and getting his students inspired in pursing their educational and career goals. His main research interests are in the area of Networks and Security and Educational Research. He teaches courses in the area of Networking, Cyber Security, Software Engineering and related technologies.

Prof. Vijayalakshmi Anand

Prof. Vijayalakshmi Anand is currently working as an Assistant Professor in t...

Prof. Vijayalakshmi Anand is currently working as an Assistant Professor in the department of computer science at BITS Pilani off campus center,Pune. She has been with BITS pilani since 2008. Before joining BITS , She was working various engineering colleges in chennnai and pondicherry. She completed her master degree in computer science from Anna University,chennai and completed her bachelore degree in Electronics &communication engineering from Manonmaniam Sundarnar university, Tirunelveli. She is pursuing Phd in crowdsourcing and machine computation for social media application at BITS Pilani Hyedarbad campus, Hyderabad. Her academics and research interest are in the area of networking and datascience .

Prof. Satya Sudhakar Yedlapalli

Prof. Satya Sudhakar Yedlapalli is an Associate Professor with the Work Integrated...

Prof. Satya Sudhakar Yedlapalli is an Associate Professor with the Work Integrated Learning Programmes(WILP) offered by the Department of Electrical and Electronics Engineering, BITS-Pilani, Pilani Campus. He is based out of the BITS-Pilani Off-Campus Center at Bangalore since August-2014. He also serves as a Group Leader for the EEE-WILP-OffCampus group since July-2021. He holds a Bachelor of Engineering in Electronics & Communications Engg. from the College of Engg., Andhra University, Visakhapatnam; a Master of Technology(M.Tech) in Communications from the Indian Institute of Technology(IIT)-Madras and a PhD in Signal Processing from the Indian Institute of Science(IISc) -Bangalore. He has about 20 years of technical work experience with the Bangalore based companies (1) Centre for Development of Telematics, (2) Motorola India Electronic Ltd, (3) Texas Instruments Pvt Ltd, (4) Samsung India R & D, (5) Saankhya Labs and (6) Aureole Technologies Pvt. Ltd. He has a passion to design and optimize computationally intensive signal processing algorithms for embedded systems and teach courses which require analytical and computational skills in the electrical engineering domain.

Prof. Akshaya Ganesan

Prof. Akshaya Ganesan is working as an Assistant Professor with the Computer ...

Prof. Akshaya Ganesan is working as an Assistant Professor with the Computer Science and Information Systems group of Work Integrated Learning Programmes Division, BITS Pilani. She has 2 years of experience in the industry and 5+ years of experience in the academia. Apart from effective classroom teaching, she has held various roles and responsibilities including curriculum development, departmental committee works, students counselling, placement training, and recruitment of faculty in various colleges. She holds an M.E Computer Science and Engineering degree from Anna University, Chennai. She teaches courses in the area of Web Development, Service Oriented Computing and Algorithms. Other area of interest includes Full stack software development, cloud computing and Devops.

Dr. Monali Mavani

Dr. Monali Mavani is working as an Assistant Professor with the Computer Science a...

Dr. Monali Mavani is working as an Assistant Professor with the Computer Science and Information Systems group of Work Integrated Learning Programmes Division, BITS Pilani. She has over 16 years of experience in academics and has served in varied roles. She received her B.Tech. degree in Electronics and Communication Engineering from SNDT University, Mumbai, India in 2002, M.E. in Computer Engineering from Mumbai University in 2013 and Ph.D. degrees in Computer Science Engineering & Information Technology from Jaypee Institute of Information Technology, Noida, India, in 2019. Her research interests include IoT, Machine Learning, and Deep Learning.

Prof. Ramakrishna Dantu

Prof. Ramakrishna Dantu is an Associate Professor in the Computer Science and Info...

Prof. Ramakrishna Dantu is an Associate Professor in the Computer Science and Information Systems department of Work Integrated Learning Programs, BITS Pilani, Hyderabad, off Campus division. Prof. Ramakrishna has a Ph.D. From University of Texas at Arlington, USA. He also holds MS in Information Systems from UTA, USA, M.Tech., IIT Bombay, and an MBA from USA. He has over 16 years of IT industry experience in USA in different domains, including Insurance and Transportation Logistics He joined BITS Pilani, Hyderabad in February, 2020. Prior to joining BITS Pilani, he worked as an Assistant Professor at California State University, Sacramento, and as adjunct faculty at University of Texas, Arlington and University of Texas at Austin. He is passionate about teaching and research. His research has been published in Information Systems Management Journal, and several international conferences.

Dr. K. Pradheep Kumar

Dr. K Pradheep Kumar is an Assistant Professor in the department of Computer Scien...

Dr. K Pradheep Kumar is an Assistant Professor in the department of Computer Science and Information Systems of Work Integrated Learning Programmes Division, BITS PIlani. He did his B.E. in Electronics and Communication from the University of Madras in 2002. He did his M.E. in Embedded Systems Technologies in 2005 and PhD in Computer Science in 2013 both from College of Engineering Guindy, Anna University. He has published patents in the domain Embedded Systems, Artificial Intelligence and Quantum Computing. He has several publications and book chapters in the domain of Data science, Artificial Intelligence, IoT and Quantum Computing. His publication in Quantum Computing has secured the Best paper award out of more than 1500 papers received by the “Society for Data Science” in 2021. During his PhD, he was the recipient of Senior Research Fellow award from CSIR. He is an Active member of professional bodies like ACM and IET.His research interests include Embedded Systems, Machine Learning, Artificial Intelligence, IoT, and Quantum Computing. Dr K Pradheep Kumar worked for two and a half years as Assistant manager (Information Technology) in a PSU bank (Union Bank of India) dealing with banking software, Cryptography and Data Security applications, He also worked as Assistant Engineer in Chennai Metrowater (a state government undertaking) in the IT department looking after Oracle data base administration for one and a half year. He has completed seven years as Assistant Professor in the department of computer science and information security in BITS. He has been actively involved in industry engagement, programme and content design, design of lab capsules and delivery of course content. He has been teaching several courses including Cloud Computing, Software Development for Portable Devices, Data Warehousing, Network Security, Real Time Operating Systems, Computer Organisation and System Software and System for Data Analytics for various programmes.

Prof. Chandra Shekar R K

Prof. Chandra Shekar R K is working as Assistant Professor in the WILP divisi...

Prof. Chandra Shekar R K is working as Assistant Professor in the WILP division, CSIS group since 2014. Previous to this, he worked in Academic Institutions (PESIT, TJIT, BTLIT - Bangalore) and IT industry (KALS - Bangalore & Firium, Maxis Communications, KL, Malaysia). He has a total experience of 22 years. He did his B.E in Computer Science and Engg. from UVCE, Bangalore, M.Tech in Computer Network and Engg. from VTU, Belgaum. Currently, he is a Ph.D scholar with BITS, Pilani, KK Birla Goa campus. His research interests are in Cloud Computing and IoT.

Prof. Sugata Ghosal

Prof. Sugata Ghosal is an off campus faculty member in the Computer Science a...

Prof. Sugata Ghosal is an off campus faculty member in the Computer Science and Information Systems department. Prior to joining BITS Pilani, he was a Senior Manager and Program Director at IBM Research – India, New Delhi. Sugata has over 20 years of research experience in areas such as computer vision, machine learning, high-performance computing, software and services analytics. He holds over 45 United States patents and has published over 40 papers in international conferences and journals. Sugata obtained a B.E. in Electronics and Telecommunication Engineering from Jadavpur University, Kolkata and a PhD in Electrical Engineering from the University of Kentucky, Lexington in 1993.

Prof. Pravin Y Pawar

Prof. Pravin Y Pawar is an Assistant Professor with the Computer Science and Infor...

Prof. Pravin Y Pawar is an Assistant Professor with the Computer Science and Information Systems group of BITS, Pilani’s Work Integrated Learning Programmes Division. He completed his ME (Software Systems) from BITS Pilani - Pilani campus and PG Diploma in Data Science from IIIT Bangalore - Upgrad. Prior to joining BITS, Pravin has worked with Oracle India Private Limited, Bangalore and Pune, for twelve and half years’ in the area of business software products development. During his tenure at Oracle, he was mostly working on the design and development of Oracle’s Streaming Analytics product with special emphasis on the visualization and dashboard designs along with analytics capabilities of the product. Pravin leads certificate programme committee as well as Full Stack Engineering programme. He teaches courses related to the Data Science and Software Engineering domain, and is also a key faculty in the M. Tech. (Data Science & Engineering) programme. He is also engaged with PG programmes in the field of AI/ML, Big Data, Business Analytics and IoT where he enjoys simplifying the courses related to the Data engineering.

Prof. Febin A Vahab

Prof. Febin A Vahab is an Assistant Professor with WILP, BITS Pilani, with 10...

Prof. Febin A Vahab is an Assistant Professor with WILP, BITS Pilani, with 10+ years of experience in industry and academics. During her tenure in IT industry, she worked in Business Intelligence domain and has worked in Customer Data Warehousing and Business Intelligence projects of major clients like NORDSTROM, WALMART etc. She did her B.Tech from Kerala University and M.Tech from VTU, Karnataka. She teaches courses in DATA SCIENCE ,Technology and Programming domains.

Dr. Chetana

Dr. Chetana is an Associate Professor in the CSIS department at Work Integrated Le...

Dr. Chetana is an Associate Professor in the CSIS department at Work Integrated Learning Programmes Division, BITS Pilani. She has more than 23 years of teaching and industry experience. She did her PhD in Computer Science and Engineering from a joint programme of IIT Bombay and Monash University, Australia. She has been working extensively on different state of art research projects and has been awarded the “Best Industry Aligned Research” at the CSI TechNext India 2019 - Awards to Academia. She has published various papers and is also a reviewer at national and international level peer reviewed conferences and journals. Her areas of expertise include Machine Learning, Natural Language Processing, Semantic Web, Deep Learning, Text Mining, Big Data Analytics, Information Retrieval and Software Engineering. Apart from teaching above courses at WILP, she is SPOC for practice school program for CSIS

Dr. Saikishor Jangiti

Dr. Saikishor Jangiti believes in “Simplify & Teach Effectively” a...

Dr. Saikishor Jangiti believes in “Simplify & Teach Effectively” and has chosen Teaching as his profession after completing his B.Tech in Computer Science & Information Technology from JNTU, Hyderabad and M.Tech in Computer Science & Engineering from Sri Venkateswara University, Tirupati. He joined the CSIS Group, Work Integrated Learning Programmes Division, BITS, Pilani with 12+ years of experience. While teaching at SASTRA Deemed University he got an opportunity to pursue research in the fields of Artificial Intelligence and Cloud Computing. He has published 17 articles in peer-reviewed international journals and conferences with a cumulative impact factor of 7.6 and awarded with the degree of Doctor of Philosophy. His subject interests include Artificial Intelligence, Data Centers, Cloud Computing, Data Mining, Web Technologies

Prof. Srinath Naidu

Prof. Srinath Naidu is an Associate Professor with the Work-integrated Learni...

Prof. Srinath Naidu is an Associate Professor with the Work-integrated Learning Programme at BITS Pilani. Srinath brings a blend of industrial and academic experience into this role with seven years of industrial experience in Electronic Design Automation companies such as Magma, Synopsys and Cadence and ten years of academic experience with IIIT Bangalore and Amrita University. He has many impactful publications in Electronic Design Automation with one of them winning the 2003 Pat Goldberg Memorial Best Paper Award from IBM Research. Srinath holds a B.Tech(Hons.) in Computer Science and Engineering from the Institute of Technology, Banaras Hindu University, Varanasi (now IIT Varanasi), M.Sc(Engg) in Computer Science from the Indian Institute of Science, Bangalore and Ph.D in Electrical Engineering from Eindhoven University of Technology in the Netherlands. Srinath's current research and teaching interests lie in the areas of machine learning, deep learning and mathematical optimization.

Prof. Rajavadhana

Prof. Rajavadhana is an Assistant Professor in the Department of Computer Science ...

Prof. Rajavadhana is an Assistant Professor in the Department of Computer Science and Information Systems at Work Integrated Learning Programmes division of BITS Pilani, India since 2016. Prior to joining BITS, she was with PSG College of Technology as Assistant Professor in the Department of Computer Science. She has over 3 years of industrial experience in the field of Enterprise Application Integration as a developer and a consultant in the BFSI domain with HCL Technologies and Larsen & Toubro Infotech. She received her B.E. & M.E. degree in Computer Science and Engineering from Anna University and MBA in Human Resource from Indira Gandhi Open University. Her research interests are in the fields of Graphs, Computer vision and computational Intelligence. She teaches courses in the area of Data Technologies and Intelligence domain.

Prof. Akanksha Bharadwaj

Prof. Akanksha Bharadwaj is an Assistant Professor and coordinator of the WILP pro...

Prof. Akanksha Bharadwaj is an Assistant Professor and coordinator of the WILP programs running with our industry partner CISCO. She is based out of the Bangalore location. Akanksha has worked for around 3 years as a Business Technology Analyst at Deloitte. She did her B.Tech in Computer Science from Bharati Vidyapeeths' college of engineering, Delhi, and M.tech from Delhi Technological University, Delhi. She teaches courses in the area of software architecture, databases, data mining, and statistics.

Prof. Vineet Garg

Prof. Vineet Garg is an assistant lecturer with BITS Pilani WILP. He has a postgra...

Prof. Vineet Garg is an assistant lecturer with BITS Pilani WILP. He has a postgraduate degree in computer science from BITS Pilani. In WILP he takes courses like Computer Networks, Network Security and Data Structures.

Dr. Lucy J. Gudino

Dr. Lucy J. Gudino received her B.E. degree in Electronics and Communication Engin...

Dr. Lucy J. Gudino received her B.E. degree in Electronics and Communication Engineering from Kuvempu University in 1994, M.Tech. and Ph.D. degrees in Computer Science and Engineering from Visvesvaraya Technological University, Karnataka, India, in 2003 and 2010 respectively. She is currently an Associate Professor with the Computer Science and Information Systems group, at BITS Pilani’s Work Integrated Learning Programmes Division. She has over 24 years of teaching experience in the field of Electronics and Communications, and Computer Science. She is a Programme Committee lead for WILP’s Computer Science and Information Systems and is involved in the design and delivery of curriculum, digitization of course and lab contents. Her research interests include Wireless Networks (Wireless Sensor Networks and Adhoc Networks), Array Signal Processing and Digital filter design. She has guided 6 PhD scholars in the areas of Wireless Sensor Networks, Wireless Adhoc Networks and Antennas for wireless applications.

Dr. Anindya Neogi

Dr. Anindya Neogi is an Associate Professor with the Work Integrated Learning Prog...

Dr. Anindya Neogi is an Associate Professor with the Work Integrated Learning Programmes, BITS Pilani. He has PhD and MS, Computer Science from State University of New York at Stony Brook and B.E. (Computer Sc. and Engg.) from Jadavpur University. After PhD, he has worked at IBM Research and subsequently IBM Software Labs for 16 years as a Research Lead for Distributed Systems Management group and Chief Architect of new products on Operations Analytics in the IBM Cloud division, respectively. Anindya was an IBM Master Inventor with 27+ issued patents responsible for IP development across IBM teams. He was also an elected Member of the IBM Academy of Technology. He has 25+ research papers with 3000+ citations. His work on virtualisation management in 2008 was awarded an ACM Middleware 10 year Test of Time award in 2018. Anindya has also worked at Rether Networks, a start-up in Berkeley, CA on real-time networks, besides a short-stint at Cadence Design Systems. Recently, he was Co-Founder and CTO of a start-up in the Cloud-based data analysis space, an initiative that he continues to mentor. He currently teaches courses in Big Data and Distributed Computing areas.

Dr. Y V K Ravi Kumar

Dr Y V K is an Associate Professor with the Compute Science and Information System...

Dr Y V K is an Associate Professor with the Compute Science and Information Systems group of Work Integrated Learning Programmes Division, BITS - Pilani. He did his M.Sc (Applied Mathematics) from Sri Venkateswara University, A.P and PhD from Osmania University, Hyderabad. He Qualified CSIR JRF/NET examination. He has published about 50 research papers in various national and international journals. Two students have been awarded PhD under his guidance.

In addition to 25 years of teaching experience, he also worked as Group 1 gazetted officer (State Civil Services) in the Ministry of Finance, Government of A.P.

As a faculty with WILP, he teaches courses like Introduction to Statistical Methods, Advanced Statistical Techniques for Analytics, Data Mining and Machine Learning for various programmes.

Prof. Chennupati Rakesh Prasanna

Prof. C Rakesh Prasanna is working as an Assistant Professor with the Compute...

Prof. C Rakesh Prasanna is working as an Assistant Professor with the Computer Science and Information Systems group of Work Integrated Learning Programmes Division, BITS Pilani. he has 10 years of experience in academia. Before joining work-integrated learning programs, he was faculty at the BITS Pilani Hyderabad campus.He has been actively involved in industry engagement, content development, and delivery of course content He holds an M.Tech GITAM University Vishakapatnam. He teaches courses in the area of Algorithms, Artificial Intelligence. Another area of interest includes machine learning.

Prof. Swarna Chaudhary

Prof. Swarna Chaudhary is an Assistant Professor in CSIS department at Work I...

Prof. Swarna Chaudhary is an Assistant Professor in CSIS department at Work Integrated Learning Programmes, BITS Pilani. She has more than 10 years of teaching and industry experience. She did her B.Tech from BIET Jhansi, a Government college affiliated to UPTU and M.Tech from DTU (earlier DCE), Delhi. Prof. Swarna Chaudhary has contributed towards designing digital content for Big Data Engineering Program (in collaboration with Upgrad), Full Stack engineering certificate programme, and IDS (Introduction to data science) course. Her research and teaching interests are in the domain of Machine Learning and Data Science

Prof. Preeti N G

Prof. Preeti NG is an Assistant Professor in the Department of Computer Science an...

Prof. Preeti NG is an Assistant Professor in the Department of Computer Science and Information Systems at Work Integrated Learning Programmes division of BITS Pilani, India since Jan 2014. Prior to joining BITS, Prof Preeti has worked in CMRIT, HKBK Bengaluru and MGIT Hyderabad as an Assistant Professor. She has an overall experience of 16+ years in Academics and Software Industry. She has earlier worked as a Software Engineer in Misys Software Solutions for about 2.5 years and as a Research Intern for an year in Intel India Software Technologies. She received her B.E. & M.Tech. in Computer Science and Engineering from Bangalore University and VTU respectively . She is passionate about teaching and strongly believes in the motto “Teaching is learning”.She has received excellent feedback for all the courses from her students and takes it to be her biggest driving force and source of satisfaction. Her research interests are in the fields of Data Mining, Machine Learning, Image Processing and Statistics. Professor Preeti has been a part of the Computer Science and Information Systems group of WILP and is involved primarily in delivery of courses in the areas of Data Technology, Intelligence and Applied Mathematics. Professor Preeti has been the Program Coordinator of Wipro, WASE programs for about 3 years since 2014 and has been coordinating SAP Labs specific MTSE program after that.

Dr. Jyotsana Grover

Dr. Jyotsana Grover is an Assistant Professor with Work Integrated Learning Progra...

Dr. Jyotsana Grover is an Assistant Professor with Work Integrated Learning Programmes, BITS Pilani. She did Ph.D, M.Tech.(Computer Applications) and M.Sc(Maths) all from IIT Delhi. She has published various papers in renowned international journals like Applied soft computing, Applied Intelligence, Pattern Recognition letters, Engineering applications of Artificial Intelligence.

Subjects Handled:

• Artificial Intelligence
• Advanced data mining
• Introduction to Data Science
• Big Data Systems

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mtech-project

Here are 30 public repositories matching this topic..., vatshayan / final-year-project-cryptographic-technique-for-communication-system.

Top B.tech/M.tech Final Year Project "Design and Analysis of Cryptographic Technique for Communication System" with Project Code, Report, PPT, Synopsis, IEEE Research Paper and HD Video Explanation

• Updated Dec 22, 2022
• Jupyter Notebook

Vatshayan / Final-Year-Blockchain-Voting-System

Blockchain based electronic voting system with Code, PPT, synopsis, Report, Research papers and full video explanation. Blockchain Final Year Project

• Updated Jan 20, 2023

Vatshayan / Face-recognition-Attendance-System-Project

Final Year Btech Face recognition Attendance System Project with code and Documents. Video Implementation with explanation too. Base IEEE paper Implementation

• Updated Aug 12, 2022

Vatshayan / Final-Year-Machine-Learning-Stock-Price-Prediction-Project

Final Year B.tech Project on Machine Learning Stock Prediction through Deep Learning

• Updated Aug 30, 2022

Vatshayan / Malware-Detection-Using-Deep-Learning-Project

Malware-Detection-System-Using-Deep-Learning-Project. Project Includes PPT. Code, Explanation Video and Documents

Vatshayan / Final-year-Project-steganography

Steganography is the technique of hiding secret data within an ordinary, non-secret, file or message in order to avoid detection; the secret data is then extracted at its destination.

• Updated Aug 14, 2022

Vatshayan / Network-Intrusion-Detection-Project

Network Intrusion Detection System Project using Machine Learning with code and Documents

Projects-Developer / Drowsiness-Detector-College-Project

Drowsiness Detector using Python. College Project with all Documents

• Updated Jan 31, 2022

Vatshayan / Steganography-Website-Project

Final Year Steganography Project with Code and Project report

Vatshayan / Blockchain-and-Cryptography-Communication-System

Final Year Blockchain Project for Security of communication. [Security of Communication Increase through Use of Combination of Cryptography and Blockchain technology]

Vatshayan / Spam-Detection-Project

Spam Detection Project for college students with PPT, Report, Code, Datasets and Research papers

Vatshayan / Heart-disease-prediction-system-Project

Heart disease prediction system Project using Machine Learning with Code and Report

Vatshayan / Stock-Price-Prediction-Project

Final year College Project with Project Report, PPT, Synopsis and Code

Projects-Developer / Heart-Diseases-Prediction-Project

Final Year Project Heart Disease Prediction Project with all Documents.

• Updated Jan 12, 2022

Vatshayan / Disease-Prediction-Project-using-Machine-Learning-Project

Diseases Prediction System though Machine Learning with code and documents

• Updated Dec 29, 2022

Vatshayan / Co2-Emission-Prediction-Using-Machine-Learning

Final year Co2 Emission Prediction Using Machine Learning Project with source code and documents. Btech Final Year Project

• Updated Feb 2, 2023

Vatshayan / Image-Recognition-Project

Beautiful Image recognition and Classification Project for final year college students.

• Updated Aug 25, 2021

Vatshayan / Decentrilized-Blockchain-Blog-System-Project

Decentrilized Blockchain Blog System Project with code and Documents

• Updated Apr 7, 2024

Projects-Developer / AI-Chatbot-Final-Year-Project

AI chatbot used to communication with End user through online on platforms such websites and application. Btech college project.

• Updated May 4, 2022

Vatshayan / Image-Chain-Blockchain-Project

Final Year ImageChain Blockchain Project is application of Blockchain. Project Include Code, Documents with Video Explanation

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Reason: Under embargo until 9 May 2025. After this date a copy can be supplied under Section 51(2) of the Australian Copyright Act 1968 by submitting a document delivery request through your library

Rethinking Franchise Contracts: The Impact of Transactional and Relational Governance Mechanisms on Franchisor Performance

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