M.Tech.

Artificial Intelligence and

Machine Learning
for Working
Professionals
INDEX
Programme introduction 01

Who should apply 02

Programme highlights 03

Programme objectives 05

WILP presence and impact 06

Student learning outcomes 07

Mode of learning 08

Experiential learning 10

Programme curriculum 12

Eligibility criteria 14

Fee structure 15

Course-wise syllabus 16

Programme structure 22

Mode of examination 24

How to apply 26

Students speak 29
 Program Introduction

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.

The market size in the Artificial Intelligence market is projected to reach

US$ 243.70 bn in 2025.

The market size is expected to show an annual growth rate (CAGR 2025-2030)
of 27.67%, resulting in a market volume of US$ 826.70 bn by 2030.

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 program equips IT professionals and software developers with

a diverse skill set, paving the way for career growth in high-demand roles like ML
Engineers and AI Scientists.

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 Who Should Apply?

IT and Software
professionals working
as Software Engineer,
Software Developer,
Programmer, Software
Test Engineer, Support
Engineer, Data Analyst,
Business Analyst, who
wish to transition to
roles such as ML
Engineers & AI
Scientists, etc. should
consider applying to this
programme

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 Programme Highlights
M.Tech. Artificial Intelligence and Meant for IT professionals and
Machine Learning is a BITS Pilani Software developers aiming to
Work Integrated Learning become expert Machine
Programme (WILP). BITS Pilani Learning Engineers & AI
Work Integrated Learning Scientists.
Programmes are UGC approved.

Pursue the four-semester The programme offers a set of

programme without any career core courses and elective
break. Contact classes over a courses, allowing students to
technology enabled platform are gain expertise in Advanced
conducted mostly on weekends Deep learning, Natural
and after business hours. Language Processing, etc.

The programme makes use of

Tools and Technologies such as
Offers the most comprehensive Tensorflow for Deep Learning and
AI & ML Curriculum for working various Python libraries for data
professionals. processing, machine learning,
OpenCV for computer vision,
NLTK for NLP etc.

The programme has an unmatched The Dissertation (Project Work)

range & depth and covers the in the final semester enables
widest variety of skill & knowledge students to apply concepts and
areas required to develop techniques learned during the
advanced AI solutions. programme.

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 The programme uses a
Continuous Evaluation System Opportunity to become a member
that assesses the learners over of an elite & global community of
convenient and regular BITS Pilani Alumni.
intervals.

The education delivery

Option to submit fee using
methodology is a blend of
easy-EMI with 0% interest and
classroom and experiential
0 down payment.
learning.

Experiential learning consists

of Virtual lab exercises,
assignments, case studies and
work-integrated activities.

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 Programme Objectives

Abundance of user-generated data, This in turn has fuelled significant job

easy access to computing and opportunities in the IT products and
storage in the cloud, open-source services sector in India and across
libraries and algorithmic the globe.
advancement have led to the
deployment of artificial intelligence
and machine learning techniques
across industries.

This program is geared towards the

professional development of
employees who are working in the
area of IT products and services
industry or who aspire for a career in
the applications of AI and ML
techniques in traditional industries.

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 WILP Presence and Impact

45+ 1,26,169
Years of Working
Educating Working Professionals
Professionals Graduated

46,178 1100+
Working Professionals
Currently Enrolled Faculty Members

47
Programmes

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 Student
Learning Outcomes
At the end of the programme, the
students will be able to:

Demonstrate conceptual
Demonstrate conceptual Understand the system
understanding and
understanding and and software engineering
hands-on knowledge of
hands-on knowledge of requirements for
traditional and
AI application areas such implementing machine
contemporary AI and
as natural language learning systems on large
machine learning
processing, computer datasets and in
techniques, including
vision, or cyber security. resource-constrained
deep learning, and
environments.
reinforcement learning.

Understand the
underlying ethical issues
in applying AI and
machine learning.

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 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:

1. The programs are designed and developed by the institute in collaboration with
industry.
2. Work-focused experiences form an active part of the curriculum.
3. 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.

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 Key Benefits of BITS Pilani WILP

● Can pursue the programme without any career break and along with the job.
● The programme curriculum is highly relevant to sectors, industries and
organisations they work for
● In addition to the institute, the learning experience of working professionals in the
programme is also supported by the employer organisation and Industry Mentors.
● 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.
● 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.
● Mid semester and End semester examinations for every semester are conducted
mostly at designated examination centres distributed across the country. For details,
click here.
● Learners can access engaging learning material which includes recorded lectures
from BITS Pilani faculty members, course handouts and recorded lab content where
applicable.

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 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.

Tools & Technologies covered

Supplementary Learning

In addition to contact classes over a technology enabled platform, supplementary sessions

will be organised periodically comprising of tutorials, doubt-clearing interactions, and
industry talks (18-20 hours per semester).

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

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

Continuous Assessment
The assessment includes graded assignments/quizzes,
mid-semester and comprehensive exam

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 Programme Curriculum
First Year - First Semester First Year - Second Semester
● Mathematical Foundations for ● Deep Neural Networks
Machine Learning
● Deep Reinforcement Learning
● Machine Learning
● Elective 1
● Introduction to Statistical Methods
● Elective 2
● Artificial and Computational
Intelligence

Second Year - First Semester Second Year - Second Semester

● Elective 3 ● Dissertation
● Elective 4
● Elective 5
● Elective 6

Pool of Electives for : Deep Pool of Electives for : NLP

Learning Specialization Specialization
● Unsupervised Deep learning# ● Natural Language Processing#
● Graph Neural Networks ● NLP Applications
● Distributed Machine Learning ● Information Retrieval
● ML System Optimization ● Speech Processing
● Fair, Interpretable, Trustworthy ● Conversational AI
Machine Learning ● Social Media Analytics
● Computational Learning Theory ● Large Language Models for
● Machine Learning on the Edge Generative AI

Note: At least 3 courses are required including the Note: # At least 3 courses are required including
course marked in# those marked with #

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 Programme Curriculum
Pool of Electives for : Pool of Electives General
Audio and Vision ● MLOps
● Computer Vision# ● Data Management for Machine Learning
● 3D Computer Vision ● Advanced Data Mining
● Audio Analysis# ● Design of Algorithms
● Video Analysis ● AI and ML for Robotics
● Computational Photography ● AI and ML Techniques for Cyber security
● Computational Imaging ● Probabilistic Graphical Models
● Multimodal Information Retrieval ● Metaheuristics for Optimization
● Contemporary Computer Graphics ● Automated Reasoning
● Quantum Machine Learning
Note: # At least 3 courses are required including
those marked with # ● Software Engineering for Machine
Learning
● Introduction to Parallel and
Distributed Programming
● API Driven Cloud Native Solutions

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.

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 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.

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 Fee Structure
Fee Structure for students admitted in Academic Year 2024-2025 is as follows:

Application Fees Admission Fees Semester Fees

(one time) (one time) (per semester)

`1500 `16,500 `71,750

Easy Monthly Payment Option with 0% Interest and 0 Down Payment

Instant EMI option with 0% interest and 0 Down Payment is now available
that allows you to pay programme fee in an easy and convenient way.

● Instant online approval in seconds

● No Credit Cards/ CIBIL score required
● Easy and secure online process using Aadhaar and PAN number
● Anyone with a Salary Account with Netbanking can apply the Option to submit fee
using easy- EMI with 0% interest and 0 down payment

Admissions Open. Last date to apply is Click here to learn more

April 7, 2025.
All the above fees are non-refundable.

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

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 Course-wise Syllabus
Mathematical Foundations for Data Science
Vector and matrix algebra, Systems of linear algebraic equations and their solutions,
Eigenvalues, eigenvectors and diagonalization of matrices, Multivariate calculus, vector
calculus, Jacobian and Hessian, multivariate Taylor series, Gradient descent,
unconstrained optimization, constrained optimization, nonlinear, optimization, Stochastic
gradient descent, Dimensionality reduction and PCA, Optimization for support vector
machines.

Introduction to Statistical Methods

Basic probability concepts, Conditional probability, Bayes Theorem, Probability
distributions, Continuous and discrete distributions, Transformation of random variables,
Estimating mean, variance, covariance, Hypothesis Testing, Maximum likelihood, ANOVA
– single factor, dual-factor, time series analysis: AR, MA, ARIMA, SARIMA, sampling
based on distribution, statistical significance, Gaussian Mixture Model, Expectation
Maximization.

Deep Neural Networks

Introduction to neural networks, Approximation properties, Back propagation, Deep
network training, Regularization and optimization, Convolution neural networks, Recurrent
neural networks, Attention models, Transformers, Neural architecture search, federated
learning, meta-learning, applications in time series modelling and forecasting, online
(incremental) learning.

Deep Reinforcement Learning

Introduction and applications, Markov decision processes (MDP), Tabular MDP planning,
Tabular RL policy evaluation , Q-learning, model-based RL, Deep RL with function
approximation, Policy search, policy gradient, fast learning, applications in game playing,
imitation learning, RL for neural architecture search, batch RL.

Unsupervised Deep Learning

Introduction to Representation Learning, PCA and variants, likelihood-based models, flow
models, autoregressive models latent variables, Deep autoencoders, Boltzmann
Machines, Generative Adversarial learning, Variants of GAN and applications,
DeepDream, neural style transfer, self-supervised learning, semi-supervised learning,
language model learning, applications in time series modelling, representation learning for
reinforcement learning, deep clustering.

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 Graph Neural Networks
Basics of graph theory, Machine learning on graphs, node embeddings, link analysis,
representation learning for graphs, Label propagation for node classification, empirical risk
minimization, graph convolutional filters, Composition with pointwise nonlinearities,
permutations, dilation and stability, transferability, graph RNN, algebraic neural networks,
applications of graph NN in subgraph mining, Recommendation systems, community
structures in networks, deep generative models, knowledge graph embeddings and
reasoning.

ML System Optimization
Review of parallel and distributed systems, System Performance Trade-offs, Distributed
machine learning for large models and datasets, General-purpose distributed computing
frameworks - Hadoop, map reduce and Apache Spark, Deep Learning frameworks and
runtimes, deep learning hardware, Deep learning compilers with optimizations, scalable
training and Inference Serving, parameter serving, Federated Learning, model
compression for optimizing communication and resource-constrained devices, Case
studies of machine learning on single GPU systems, on GPU Clusters.

Fair Interpretable Trustworthy Machine Learning

Biases and fairness, Fair representation learning, Interpretability and Transparency,
Example and Visualization Based Methods for Interpretability, Interpreting deep neural
networks, Fairness Through Input Manipulation, Fair NLP/Vision, Robustness and
adversarial attacks/defence, ML auditing, privacy.

NLP Applications
Sentiment Analysis, Grammar and Spelling Checkers, Cross Lingual Language Models,
Machine Translation including Indic Languages, Question answering and Chatbots,
Information extraction (named entity recognition, relation extraction), Knowledge graph.

Social Media Analytics

Social Media Platforms, NLP in SMA, Text Summarization, Opinion Science and
dynamics, ML/DL in SMA- Community detection, Ethical Social Media, Case Studies- Role
of social media in disaster management, SM driven mental health and behaviour Analysis.

MLOps
Adaptation of DevOps for building and deploying machine learning systems, Model
Deployment: Infrastructure requirements, Deployment patterns, Model CI/CD (Build, Test,
Integration and Delivery of model), Model Serving tools and technologies, Model life cycle
management, ML pipelines with data management support, model assessment, evolution,
and management in production, MLOps infrastructure and tools, Trends in Model
deployment: ML on the Cloud / Edge / Browsers, VMs, Containers, Docker, Kubernetes
(K8S), FaSS; ML-as-a-Service.

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 Computer Vision
Image formation, structure, and transformations, Low-level (filters, features, texture),
Mid-level(segmentation, tracking, morphology) and High-Level Vision (registration contour
geometry, Object detection and classification, segmentation), Deep learning for object
detection, Recognition, Face detection and face recognition, Facial key point recognition,
Optical Character, Recognition, Visual annotation, Activity recognition, Applications for
autonomous cars – Landmark detection and tracking, track pedestrians, 3D projection,
Image search and retrieval, Edge devices for computer vision

Probabilistic Graphical Models

HM, Markov Random Field, Bayesian networks, Representation, Learning, Inference,
Dynamic Bayesian Networks and Temporal Bayesian networks, applications.

Data Management for Machine Learning

Data Models and Query Languages: Relational, Object-Relational, NoSQL data models,
Declarative (SQL) and Imperative (MapReduce) Querying, Data Encoding: Evolution,
Formats, Models of dataflow, Machine learning workflow, Data management challenges in
ML workflow, Data Pipelines and patterns, Data Pipeline Stages: Data extraction,
ingestion, cleaning, wrangling, versioning, Transformation, exploration, feature
management, Modern Data Infrastructure: Diverse data sources, Cloud data warehouses
and lakes, Data Ingestion tools, Data transformation and modelling tools, Workflow
orchestration platforms, ML model metadata and Registry, ML Observability, Data privacy
and anonymity.

Natural Language Processing

Natural Language Understanding and Generation, N-gram and Neural Language Models,
Introduction to LLM, Introduction to prompt engineering, Word to Vectors / Word
Embedding (Skip gram/CBOW, BERT), Part of Speech Tagging, Parsing, Word Sense
Disambiguation, Semantic Web and Knowledge Graph, Introduction to Retrieval
Augmented Generation (RAG).

Video Analysis
Digital Video, Spatio temporal sampling, Low-Level Features to High-Level Semantics,
Video enhancement technologies (denoising, stabilization, unsharp masking,
super-resolution), Background modelling and Foreground Detection; ML techniques for
Video Motion Detection, Tracking, Compression, Indexing and Retrieval, Browsing and
Summarization, Applications in License plate detection on moving vehicles, monitoring
traffic jams, Activity recognition, Crowd management and gesture recognition.

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 Information Retrieval
Organization, Representation and access to information, Categorization, indexing, and
content analysis, Data structures for unstructured data; design and maintenance of such
data structures, Indexing and indexes, retrieval and classification schemes; use of codes,
formats, and standards; analysis, construction and evaluation of search and navigation
techniques; search engines and how they relate to the above, Multimedia data and their
representation and search.

Conversational AI
Intro to conversational AI. Use cases of chatbots, NLU and Dialog Management, Design
the flow of conversation, Crafting training data, Training the NLU model, Understanding
Dialog Management, Intent classification and entity extraction, Using slots for context
understanding, Understanding NLU components, Supporting multiple languages, Voice
bots, Testing the bot, Failing gracefully with fall back action

Artificial and Computational Intelligence

Introduction to Intelligent Agents, Search-based agents - Informed and Uninformed
searches, Local Search Algorithms - Hill Climbing, Simulated Annealing, Local Beam
Search, Genetic Algorithms, ACO, PSO, Minimax Algorithm, Alpha Beta Pruning,
Knowledge Representation and Reasoning: Logical Agents - Representation and
reasoning using propositional and predicate logic, Resolution, forward and backward
chaining, DPLL, Probabilistic Reasoning - Knowledge representation using Bayesian
networks, exact and approximate inference from bayesian networks, Hidden Markov
Models, Ethics in AI: Explainable AI

Machine Learning
Introduction to Machine Learning, Various kinds of learning, Supervised Learning,
Unsupervised Learning, Model Selection; Bayesian Learning, MAP Hypothesis, MDL
Principle, Bias Variance, Decomposition, Bayes Optimal Classifier, Naive Bayes Classifier;
Linear Models for Regression, Linear Models for Classification; Non-Linear models,
Decision trees; Instance Based Learning, KNN Algorithm, Support Vector Machines,
Ensemble methods: Random Forest, Bagging, Boostin.

AI and ML Techniques for Cyber Security

Introduction to Cyber-Security, Supervised Learning for Misuse/Signature Detection,
Machine Learning for Anomaly Detection, Malware detection and classification; Network
Intrusion detection and classification, Detection and categorization of domain names;
Profiling Network Traffic; Adversarial Machine Learning for Malware detectiong.

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 Quantum Machine Learning
The course focuses on Quantum computing applications particularly with respect to
Machine learning and artificial intelligence. It showcases real time applications based on
quantum machine learning. Insights on data science models using qubits and quantum
data sets are explained. It also describes different quantum machine learning algorithms
with optimisations that need to be carried out in the classical computing domain for
implementing the paradigm shift.

Large Language Models for Generative AI

Training Paradigm of LLM; Optimizing LLM, Multi-Query Attention, Grouped-query
attention, Quantization, Pruning, Distillation; Fine-Tuning Parameter Efficient Fine Tuning
(PEFT), Instruction fine Tuning, Preference tuning, Reinforcement Learning from Human
Feedback (RLHF), Evaluating LMs; Prompting and In-context Learning-prompting
techniques, LLM reasoning, Chain-of-Thought Prompting, Tree-of-Thought, Evaluating
Prompted LMs; Enhancing Pre-trained LMs using additional knowledge-Augmenting LMs
using Knowledge Graph, Textual Encoding of Tables, Retrieval-based LMs, Multimodal LMs
- Vision LMs, Popular multimodal LMs, LMs of Code; Small Language Models; Bias,
Hallucinations and Toxicity, Detoxification techniques, Privacy attacks of LLMs,
Memorization, Prompt Hacking, Adversarial attacks.

Multimodal Information Retrieval

Encompasses fundamental concepts of information retrieval (IR), expanding into
multimodal approaches that combine real-world text, images, audio, video, and other forms
of data. Topics include: Introduction to Multimodal Information Retrieval, Fundamentals of
Information Retrieval, Textual Information Retrieval and Pre-processing, Introduction to
Multimodal Systems, Feature Extraction for Visual Data (Images), Visual Information
Retrieval (VIR), Audio Information Retrieval, Video Information Retrieval (VIR), Multimodal
Retrieval: Early Fusion, Multimodal Retrieval: Late Fusion, Cross-Modal Retrieval, Deep
Learning for Multimodal Information Retrieval, Evaluation of Multimodal Retrieval Systems,
Challenges and Future Directions in Multimodal IR.

Contemporary Computer Graphics

Basics of Computer Graphics, Applications of machine learning, Image Representation,
Geometrical Transformations in 2D and 3D, 3D Modeling and Scene Representation,
Shape recognition with AI, Machine learning for texture mapping and segmentation,
Rendering Basics, ML for denoising in ray tracing, deep learning for image super
resolution, Animation Basics, AI for motion capture data processing, Reinforcement
learning for animation and game physics, Texture Mapping, Generative Adversarial
Networks (GANs) for texture generation, Deep learning for material recognition and
synthesis, AI in automated 3D object recognition and placement in scenes, Computer

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 Vision Algorithms for 3D graphics, Deep learning for pose estimation and tracking in 3D
graphics, Real-time Rendering, AI for procedural content generation, ML for rendering,
Generative Adversarial Networks (GANs) for image generation, Style transfer in images and
animations, GANs for generating realistic 3D models, AI in automated 3D object recognition
and placement in scenes, AI-based colorization and artistic effects, VR rendering
techniques, head-tracking, and user interaction, AI for object recognition in AR, Deep
learning for AR scene reconstruction and interaction, Deep learning for automatic photo
editing and enhancement AI-based image stitching and panorama generation, Generative
models for landscapes, cityscapes, and environments.

Software Engineering for Machine Learning

Machine Learning in Production: Introduction, From models to systems, Machine learning
for software engineers; Requirements Engineering: When to use ML, Gathering
requirements; Architecture and Design: Thinking like a software architect, Quality attributes
of ML components, Automating ML pipeline, Scaling the system, Planning for operations;
Coding practices: What is good code, Analyzing code performance, Using data structures
effectively, OOP and functional programming, Errors, Logging & Debugging, Code
formatting and linting, documentation, APIs; Sharing code: Version control, Dependencies,
Packaging; Testing and Quality Assurance: Types of tests, Testing for ML, Testing model
training, Testing model inference, Model quality, Data quality, Pipeline quality, System
quality, Testing and experimenting in production; Automation and Deployment; Security for
Machine Learning; Process and Teams: Data science and software engineering process
models, Interdisciplinary teams; Responsible ML Engineering: Responsible Engineering,
Provenance, Reproducibility, Interpretability, Security and Privacy.

Machine Learning on the Edge

Introduction to Edge AI, Edge AI applications and use cases, Edge AI ecosystem, Hardware
platforms overview; Edge Hardware and Architecture, Microcontrollers and embedded
systems, Edge processors (ARM, RISC-V), AI accelerators, Edge TPU, Neural compute
sticks, Power consumption considerations. Model compression overview, Quantization
techniques, Pruning methods, Knowledge distillation. Neural architecture search,
Lightweight model architectures, MobileNet family, EfficientNet family; Small Language
Models, Edge AI Frameworks and Tools, Edge AI development tools, Model conversion and
optimization tools; Embedded Machine Learning, TinyML concepts, Model deployment on
MCUs, Resource-constrained computing; Edge AI Security and Privacy; Performance
Optimization - Latency, Memory, Power, Benchmarking and Performance metrics.

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.

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 Programme Structure
Core Courses (6)

Course Title
● Mathematical Foundations for Machine Learning
● Introduction to Statistical Methods
● Artificial and Computational Intelligence
● Machine Learning
● Deep Neural Networks
● Deep Reinforcement Learning

Pool of Electives Deep Learning Specialization

Course Title
● Unsupervised Deep learning#
● Graph Neural Networks
● Distributed Machine Learning
● ML System Optimization
● Fair, Interpretable, Trustworthy Machine Learning
● Computational Learning Theory
● Machine Learning on the Edge
# At least 3 courses are required including the course marked in #

Pool of Electives NLP Specialization

Course Title
● Natural Language Processing #
● NLP Applications
● Information Retrieval
● Speech Processing
● Conversational AI
● Social Media Analytics
● Large Language Models for Generative AI
# At least 3 courses are required including those marked with #

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 Pool of Electives - Audio and Vision
Course Title
● Computer Vision #
● 3D Computer Vision
● Audio Analysis #
● Video Analysis
● Computational Photography
● Computational Imaging
● Multimodal Information Retrieval
● Contemporary Computer Graphics
# At least 3 courses are required including those marked with #

Pool of Electives - General

Course Title
● MLOps
● Data Management for Machine Learning
● Advanced Data Mining
● Design of Algorithms
● AI and ML for Robotics
● AI and ML Techniques for Cyber security
● Probabilistic Graphical Models
● Metaheuristics for Optimization
● Automated Reasoning
● Quantum Machine Learning
● Software Engineering for Machine Learning
● Introduction to Parallel and Distributed Programming
● API Driven Cloud Native Solutions

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 Mode of Examination

Semester 1, 2 and 3 have Mid-Semester

Examinations and Comprehensive
Mode of Examinations Examinations for each course.
applicable for students These examinations are mostly
scheduled on Friday, Saturday or
admitted in
Sunday. Students need to appear in
person for taking the examinations
Batch beginning in at the institution’s designated
examination centres as per the
April 2025. examination schedule, Instructions, rules
and guidelines announced before every
examination.

During these semesters,

in addition to the above
mentioned Mid-Semester and
In Semester 4 (Final
Comprehensive examinations,
Semester), the student will be
there will also be
Quizzes/Assignments conducted doing Dissertation/Project
online on the Learning Work as per the Institution’s
Management System (LMS) as per guidelines.
the course plan in which the
students need to participate.

Students can take their examination at any of our 36 designated examination centres in India
at the following locations:
● South Zone: Bangalore, Chennai, Hyderabad, Mysore, Vijayawada, Visakhapatnam, Kochi,
Thiruvananthapuram, Hosur, Madurai, Kancheepuram, Coimbatore.
● North Zone: Delhi NCR, Gurugram, Noida, Faridabad, Jaipur, Chandigarh, Lucknow,
Bhilwara, Udaipur, Pilani.
● West Zone: Mumbai, Thane, Pune, Ahilya Nagar, Goa, Ahmedabad, Vadodara, Surat,
Indore, Nagpur.
● East Zone: Kolkata, Guwahati, Jamshedpur, Bhubaneswar.
In addition to these locations, the Institution also has a designated examination
centre in Dubai.

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 For International Students:
● In addition to the above locations, the institution also has a designated international
examination centre, located in Dubai.
● To facilitate the learning of international students, applying from any other location except
India and Dubai, the mode of examinations will be online, which can be availed by meeting
the requirements of the institute.
Requirements for online examinations
o Scanned copy of the visa for the country in which you are currently residing. The visa
should be currently valid. No expired visas shall be considered,
(OR)
o Scanned copy of government-issued ID from the residing country,
(And)
o HR recommendation or endorsement letter from the employer, stating the location of
your work.
● Indian students, who are temporarily based out of India, can also avail of online examinations
on request by meeting the above-mentioned requirements of the institute.

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 How to Apply

Click here to Create your login at the

apply now Application Center by
Once logged in,
entering your unique
through the BITS follow four essential
Email id and create a
Pilani online steps:
password of your
application centre. choice.

Step 1 2 3 4
Fill and submit
Step
Download a PDF
Step
Pay the application
Step
Print the downloaded
your application copy of the fee of INR 1,500 using Application Form and
form for your application form. Net banking/Debit note your Application
chosen program. Card/Credit Card. Form Number.

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.

On page 4, complete the Mentor Consent Form, Due to remote work policies, some candidates
which needs to be signed by your Mentor. may struggle to get physical mentor signatures.
They can request email approval using a
provided format.

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 Role of a Mentor:
The basic role of the mentor will be to ensure that the student remains aligned with the
academic objectives and the key academic milestones while pursuing the programme. The
mentor’s valuable guidance and professional expertise would also be leveraged to maximise
work-integrated learning and make the education experience highly relevant for the job role
and pursuit of the long-term career goals of the student. Following are the expected
responsibilities of the mentor:
1. Periodically monitor student’s study schedules and submission deadlines for the
programme.
2. Provide guidance towards integrating learning from the programme with job role/long-term
career goals, especially while the student pursues their learning assignments and project
work.
3. Monitor the student’s progress throughout the duration of the programme. If required by
BITS Pilani, also try to be available to engage with the faculty to collaboratively assess
the student’s academic performance and recommend any learning improvement plan.
4. Emphasise the importance of self-study and self-learning throughout the programme to
the student.
Qualifications for a Mentor:
The mentor chosen should be a senior professional with at least 5 years of relevant work
experience, holding a B.E./ B.Tech./ M.Sc./ M.B.A./ M.B.B.S./ First Degree at BITS Pilani or
its equivalent. If the mentor has less than 5 years of relevant work experience, then the
minimum educational qualification for the mentor should be M.E./ M.Tech./ M.S./ M.Phil./
Higher Degree of BITS Pilani or its equivalent is required.

In the final step (Step 4),

Photocopies of these
Page 5 of the downloaded upload your printed Application Accepted file formats for
documents should be made,
Application Form includes Form, Mentor Consent Form, uploads include .DOC,
and applicants need to
a Checklist of Employer Consent Form, and .DOCX, .PDF, .ZIP, and
self-attest academic mark
Enclosures/Attachments. mandatory documents .JPEG.
sheets and certificates.
one by one.

Selected candidates will

The Admissions Cell will You can also check your
receive email notifications
review your application for selection status by logging
within two weeks of submitting
completeness, accuracy, in to the Online Application
their application with all
and eligibility. Centre.
required documents.

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 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 BITSPilani 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 09.09.20 has approved the continued offering of BITS
Pilani’s Work Integrated Learning programmes.

The material in this brochure is provided for educational and informational purposes only. All the
images that have been used belong to their respective owners and have been picked up from the
public domain.

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 Students Speak
BITS Pilani WILP provided me with the opportunity to pursue a structured
Master's programme without me having to leave my job. I was able to enhance
my expertise in Python, Machine Learning, NLP, and Deep Learning, enabling
me to successfully transition from a business consultant to a project engineer
specializing in AI. Further, the faculty at BITS Pilani WILP were outstanding and
had rich industry experience. The flexibility of online lectures and access to
recorded sessions enabled me to effectively balance my professional, personal,
and academiccommitments.

Anand Jha
Lead Data Engineer - AI, Intellect Design Alumnus

BITS Pilani WILP enabled me to enhance my skills while continuing to work,

providing a comprehensive understanding of Data Mining, Machine Learning, AI,
and Big Data Systems, which significantly boosted my problem-solving and
analytical abilities. The hands-on tools and remote labs bridged the gap between
theory and practice, allowing me to confidently apply advanced technologies to
real-world challenges. For my final semester project, I focused on cross-selling
products by analyzing customer data and behavior, integrating data mining and
machine learning techniques to turn theoretical concepts into actionable insights
and showcasing the programme’s real-world relevance.

Nikhila
Senior Data Analyst, Citi Alumnus

BITS Pilani WILP played a key role in my transition from an SAP developer to an
AI architect, providing me with industry-relevant skills that enabled me to handle
complex projects and contribute at a higher level within my organization, all while
seamlessly balancing work and academics. The programme’s focus on
continuous learning expanded my horizons, giving me the confidence to explore
new opportunities. The combination of theoretical knowledge, practical tools, and
strong mentor support made this journey a key milestone in both my personal
and professional development.

Manasa
AI Architect Alumnus

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B2C_04_03_2025
Let's start a conversation
to ignite the change you desire

https://bits-pilani-wilp.ac.in

Call: 080-48767777
admission@wilp.bits-pilani.ac.in