Programme Overview
The M.Tech in Artificial Intelligence and Machine Learning is a four-semester programme that combines a strong academic core with advanced elective pathways and a 16-unit dissertation. Students may pursue a specialization in Deep Learning, Natural Language Processing, or Audio and Vision, or choose from the General elective pool to build broader interdisciplinary depth. The curriculum is designed to balance foundational rigour with emerging areas in AI and ML, enabling learners to develop both depth in a specialization and flexibility across the discipline.
Completion of at least three courses from a specialisation track will have the specialisation explicitly mentioned on the final degree certificate; students in the General Electives category receive the M.Tech (AIML) degree without a specialisation designation.
Programme Objectives
- Build strong foundations in AI, Machine Learning, Deep Learning, and mathematical reasoning.
- Develop expertise in Natural Language Processing, Deep Learning, Audio and Vision
- Enable learners to design, deploy, and manage scalable production-grade AI systems using MLOps and cloud-native technologies.
- Equip professionals to build intelligent solutions using state of art areas in Generative AI, LLMs, Agentic AI, Multimodal AI systems, Computer Vision, AIML for Robotics, Quantum ML, Edge AI, and Conversational AI.
- Foster skills in secure, responsible, and research-driven AI for real-world industry applications and innovation.
Programme Structure
| Semester | Courses | Units |
|---|---|---|
| Semester I | Core Courses | 17 |
| Semester II | 2 Core + 2 Electives | min 16 |
| Semester III | 4 Electives | min 16 |
| Semester IV | Dissertation | 16 |
- Mandatory Semester II: ZG530 Natural Language Processing
- At least 3 NLP-track courses to be completed across Semester II & III
- Mandatory Semester II: ZG533 Unsupervised Deep Learning
- At least 3 DL-track courses to be completed across Semester II & III
- Mandatory Semester II: ZG525 Computer Vision
- Mandatory Semester III: ZG539 Audio Analysis
- At least 3 AV-track courses to be completed across Semester II & III
Curriculum Focus
Semester I
The first semester lays the mathematical and computational foundation essential for all subsequent learning in the programme. Courses in Mathematical Foundations for Machine Learning and Introduction to Statistical Methods equip students with the analytical tools — linear algebra, calculus, probability, and statistical inference — that underpin every AI and ML algorithm. Machine Learning introduces the core paradigms of supervised, unsupervised, and instance-based learning, building intuition for model selection and evaluation. Deep Neural Networks then bridges classical ML with modern deep learning, covering architectures such as CNNs, RNNs, transformers, and attention models that are prerequisites for the advanced electives in Semesters II and III. Together, these four courses ensure that students enter the elective phase with the theoretical depth and practical readiness to engage with specialised topics confidently.
Preparatory Courses (Optional)
Prior to Semester I, students are encouraged to complete two optional preparatory courses available on the platform. While optional, these courses are strongly recommended to help students meet the academic standards set by BITS Pilani from the very first semester.
| Course | Purpose | Type |
|---|---|---|
| Introduction to Python | Builds programming fluency required for assignments and lab work across the programme. | Audit |
| Mathematical Foundation | Covers foundational pre-university mathematics as a stepping stone to the rigour of Mathematical Foundations for Machine Learning. | Audit |
Core Courses
| Code | Course | Units | Type |
|---|---|---|---|
| AIML*ZC416 | Mathematical Foundations for Machine Learning | 4 | Core |
| AIML*ZC418 | Introduction to Statistical Methods | 4 | Core |
| AIML*ZG565 | Machine Learning | 4 | Core |
| AIML*ZG511 | Deep Neural Networks | 5 | Core |
Evaluation Components
The programme follows a continuous evaluation model designed to reflect the academic rigour of BITS Pilani. Students are assessed across three components throughout the semester, and consistent engagement from the first session is strongly recommended. Relying solely on exam preparation is insufficient — the volume and depth of content requires sustained effort across the semester.
| Component | Description | Syllabus Coverage |
|---|---|---|
| EC1 – Quizzes, Assignments & Mini-projects | Conducted online through Taxila LMS. Refer to the EC1 Proforma uploaded on the course page in Taxila LMS for schedule and details. | Announced on LMS per quiz |
| EC2 – Mid-Semester Test (Closed Book) | Written examination held at designated centres. | Session Nos. 1–8 |
| EC3 – Comprehensive Exam (Open Book) | Written examination held at designated centres. Authorised textbooks and pre-approved material permitted. | Session Nos. 1–16 |
Your Learning Pathway
Attend Online Sessions
Engage actively in weekend sessions via MS Teams. Interact with the Professor/Instructor in charge and Instructors during the session to clarify concepts and build understanding.
Participate in Webinars
Attend weekday webinars led by Learning Facilitators for hands-on coding demonstrations, problem-solving tutorials, and assignment guidance.
Raise Doubts on the Discussion Forum
Use the Q&A discussion forum on Taxila LMS to clarify concepts between sessions. Forums are monitored by the Learning Facilitator and responses are shared for the benefit of all students.
Follow the EC1 Schedule
Attempt quizzes, assignments, and mini-projects as per the EC1 Proforma available on the course page. Consistent performance in EC1 contributes significantly to your final grade.
Complete Lab Exercises
Work through lab sheets available in the Labware section of Taxila LMS to build hands-on programming and analytical skills essential for assignments and exams.
Utilise Library and Virtual Lab Resources
Make full use of BITS Pilani's digital library and virtual lab infrastructure available to all students. These resources support deeper reading, experimentation, and project work throughout the semester.
Prepare for Examinations
With consistent engagement through the above steps, students will be well-prepared for the EC2 Mid-Semester Test and the EC3 Comprehensive Exam. Refer to the course handout for syllabus coverage and exam guidelines.
Tips for Working Professionals
Plan Your Weekly Study Hours
Semester I carries 17 units, where 1 unit equals 32 hours of effort over the semester. This translates to approximately 15–18 hours of study per week across all four courses, in addition to weekend online sessions. Plan your week in advance and protect study time as you would a professional commitment.
Do Not Fall Behind
The curriculum is cumulative — concepts introduced in early sessions are built upon in later ones. Missing sessions or delaying self-study creates a compounding backlog that is difficult to recover from, especially before EC2 and EC3.
Use Your Workplace as a Learning Context
Where possible, relate assignments and mini-projects to problems in your organisation. This situated learning approach not only deepens understanding but also produces work of direct professional relevance, and is particularly valuable when choosing a dissertation topic in Semester IV.
Engage With Your Peers
Group assignments are an opportunity to learn from colleagues across industries and organisations. Active participation in group work and discussion forums enriches learning beyond what individual study can provide.
Reach Out Early
If you find a concept difficult or are falling behind on an assignment, contact your Learning Facilitator through the discussion forum without delay. Early intervention is far more effective than last-minute revision before examinations.
Semester II
The second semester introduces two core courses in Artificial and Computational Intelligence and Deep Reinforcement Learning, building on the foundations of Semester I. Students can select any two electives in this semester. These can be from the chosen specialisation track (NLP, Deep Learning, or Audio and Vision) or from the General Electives.
Specialisation Tracks – Semester II
- Mandatory Semester II: ZG530 Natural Language Processing
- At least 3 NLP-track courses to be completed across Semester II & III
- Mandatory Semester II: ZG533 Unsupervised Deep Learning
- At least 3 DL-track courses to be completed across Semester II & III
- Mandatory Semester II: ZG525 Computer Vision
- Mandatory Semester III: ZG539 Audio Analysis
- At least 3 AV-track courses to be completed across Semester II & III
Rationale for Semester II Core Courses
The two core courses of Semester II build directly on the machine learning foundations established in Semester I. Artificial and Computational Intelligence broadens the scope beyond statistical ML, introducing classical AI techniques including search algorithms, knowledge representation, probabilistic reasoning, fuzzy logic, and genetic algorithms — equipping students with a fuller picture of the AI landscape. Deep Reinforcement Learning then introduces a distinct and powerful learning paradigm where agents learn through interaction with an environment, covering Markov decision processes, policy gradient methods, and deep RL — a critical area underpinning modern AI applications in gaming, robotics, and autonomous systems. Together, these two courses ensure students have both the breadth of classical AI and the depth of modern reinforcement learning before engaging with specialisation electives.
Core Courses
| Code | Course | Units | Type |
|---|---|---|---|
| AIML*ZG557 | Artificial and Computational Intelligence | 4 | Core |
| AIML*ZG512 | Deep Reinforcement Learning | 4 | Core |
Planning Your Electives
Elective selection in Semester II is one of the most consequential academic decisions in the programme. Students must choose two electives that align with their intended specialisation track, as certain electives are mandatory for specific specialisations. Since specialisation requirements span both Semester II and Semester III, students are strongly advised to review the full elective list for both semesters together before registering. Changing electives after ERP registration is not permitted. Refer to the Overview tab for the complete specialisation requirements and plan your four-semester elective path before the registration window opens.
Electives on Offer
| Code | Course | Units | Specialisation |
|---|---|---|---|
| AIML*ZG530 | Natural Language Processing | 4 | NLPMandatory |
| AIML*ZG537 | Information Retrieval | 4 | NLP |
| AIML*ZG516 | ML System Optimization | 4 | DL |
| AIML*ZG533 | Unsupervised Deep Learning | 4 | DLMandatory |
| AIML*ZG525 | Computer Vision | 4 | AVMandatory |
| AIML*ZG540 | Video Analysis | 4 | AV |
| AIML*ZG567 | AI and ML Techniques for Cyber Security | 5 | Elective |
| AIML*ZG509 | Architecting AI Systems | 4 | Elective |
| AIML*ZG526 | Probabilistic Graphical Models | 4 | Elective |
| AIML*ZG543 | Multimodal Information Retrieval | 4 | Elective |
| AIML*ZG529 | Data Management for Machine Learning | 4 | Elective |
Choosing Your Specialisation Track
Students with aspirations in language technologies, conversational systems, text analytics, or multilingual applications may find the NLP Specialisation most aligned with their learning goals.
Those drawn towards building and understanding advanced neural architectures, generative models, or scalable distributed learning systems may find the Deep Learning Specialisation a better fit.
Students interested in visual intelligence, video understanding, audio processing, or multimodal perception will find the Audio and Vision Specialisation most relevant to their aspirations.
The General Electives suit students who wish to explore across these domains or whose interests span multiple areas without a single dominant focus.
Elective Buckets — Semester II (October 2026 onwards)
Semester III
Semester III is the final coursework semester and the most consequential in shaping a student's specialisation profile. Students select four electives entirely from the elective pool, completing their specialisation track or broadening their interdisciplinary learning through the General Electives. The elective choices made here, in combination with Semester II selections, determine the specialisation designation on the final degree certificate. Semester III is also the ideal time to align coursework with the dissertation topic — students are encouraged to choose electives that deepen expertise in the domain they intend to pursue in Semester IV.
Specialisation Tracks – Semester III
- Mandatory Semester II: ZG530 Natural Language Processing
- At least 3 NLP-track courses to be completed across Semester II & III
- Mandatory Semester II: ZG533 Unsupervised Deep Learning
- At least 3 DL-track courses to be completed across Semester II & III
- Mandatory Semester II: ZG525 Computer Vision
- Mandatory Semester III: ZG539 Audio Analysis
- At least 3 AV-track courses to be completed across Semester II & III
Elective Selection Guidance
By Semester III, students have completed the core programme and two electives from Semester II. This semester offers the opportunity to complete the specialisation track and simultaneously lay the groundwork for the dissertation. Students are strongly advised to identify a potential dissertation problem area before finalising their Semester III electives — selecting courses that build directly relevant knowledge will significantly strengthen the dissertation. Students who have not yet committed to a specialisation should use this semester to consolidate their choices, as this is the last opportunity to meet specialisation requirements. Refer to the Overview tab for specialisation requirements and consult the dissertation research areas in the Dissertation tab for guidance on aligning electives with potential dissertation topics.
Electives on Offer
| Code | Course | Units | Specialisation |
|---|---|---|---|
| AIML*ZG521 | Conversational AI | 4 | NLP |
| AIML*ZG536 | Large Language Models for Generative AI | 4 | NLP |
| AIML*ZG519 | NLP Applications | 4 | NLP |
| AIML*ZG522 | Social Media Analytics | 4 | NLP |
| AIML*ZG520 | Speech Processing | 4 | NLP |
| AIML*ZG578 | Advanced Reasoning and Planning | 4 | NLP |
| AIML*ZG518 | Computational Learning Theory | 4 | DL |
| AIML*ZG534 | Fair, Interpretable, Trustworthy ML | 4 | DL |
| AIML*ZG514 | Graph Neural Networks | 4 | DL |
| AIML*ZG535 | Machine Learning on the Edge | 4 | DL |
| AIML*ZG515 | Distributed Machine Learning | 4 | DL |
| AIML*ZG539 | Audio Analysis | 4 | AVMandatory |
| AIML*ZG538 | 3D Computer Vision | 4 | AV |
| AIML*ZG541 | Computational Photography | 4 | AV |
| AIML*ZG580 | Multimodal AI | 4 | Elective |
| AIML*ZG579 | Agentic AI Systems | 4 | Elective |
| AIML*ZG528 | AI and ML for Robotics | 4 | Elective |
| AIML*ZG549 | API Driven Cloud Native Solutions | 5 | Elective |
| AIML*ZG523 | MLOps | 4 | Elective |
| AIML*ZG545 | Quantum Machine Learning | 4 | Elective |
| AIML*ZG546 | Software Engineering for Machine Learning | 4 | Elective |
Choosing Your Specialisation Track
By Semester III, your specialisation direction should be taking shape based on your Semester II elective choices. The following guidance will help you complete your track or consolidate your interdisciplinary profile.
NLP Specialisation
If you chose Natural Language Processing in Semester II, Semester III is where you deepen your expertise in language technologies. Students with aspirations in conversational AI, generative language systems, multilingual applications, or text analytics should complete their NLP track here. Consider aligning your remaining electives with your dissertation domain.
Deep Learning Specialisation
If you chose Unsupervised Deep Learning in Semester II, Semester III is where you complete your deep learning profile. Students drawn towards theoretical foundations may combine Computational Learning Theory with Fair, Interpretable, Trustworthy ML. Those interested in scalable and deployable systems may lean towards Graph Neural Networks and Machine Learning on the Edge.
Audio and Vision Specialisation
If you chose Computer Vision in Semester II, Semester III is where you complete your visual and auditory intelligence profile. Audio Analysis is mandatory for this specialisation. Students with aspirations in 3D perception, computational imaging, or multimodal systems should select electives accordingly.
General Electives
If you are in the General Electives, Semester III is your opportunity to either consolidate a thematic profile across domains or explore new areas entirely. Students with dissertation aspirations in AI systems, responsible AI, or applied ML may use this semester to build directly relevant expertise.
Elective Buckets — Semester III (October 2026 onwards)
Dissertation (Semester IV)
The dissertation is a 16-unit independent research project completed in Semester IV. It represents the culmination of the programme — an opportunity for students to apply the knowledge and skills acquired across three semesters to a substantive, original problem of professional and academic relevance.
Eligibility
Grading
The dissertation is graded on a non-letter scale: Excellent, Good, Fair, and Poor. These grades do not contribute to the student's CGPA. Successful completion of the dissertation is mandatory for the award of the M.Tech degree.
| Component | Weightage |
|---|---|
| Abstract / Outline | 10% |
| Mid-Semester Report | 30% |
| Final Report | 60% (of which 10% is organisation mentor feedback) |
Students who appear for the final Viva may be graded RRA under the following conditions: inability to defend the work; inadequate technical competence; incomplete or inadequate report; or plagiarised work.
Dissertation Requirements and Scope
Dissertation using Applied Research Approach
Applied Research Project: A supervised research project in which students investigate a real-world problem, review relevant literature, design and implement AI/ML solutions, conduct experiments, evaluate results using appropriate metrics, and demonstrate practical impact through a prototype, publication, or industry-relevant outcome.
The M.Tech dissertation is expected to demonstrate applied research — a disciplined process of identifying a real-world problem, investigating existing knowledge and techniques, and designing, implementing, and evaluating a solution. This is distinct from a routine implementation project or a mini-project in scope, rigour, and originality.
A well-chosen dissertation problem typically has a specific challenge or constraint that makes it non-trivial. This challenge becomes the research driver — it forces the student to go beyond known solutions, survey the state of the art, and justify the design choices made.
Dissertation vs Mini-project vs Assignment
- An assignment implements a known solution to a known problem within a defined scope.
- A mini-project extends this by applying techniques to a new dataset or context, with limited investigation and evaluation.
- A dissertation identifies a problem with a specific challenge or constraint, investigates existing approaches, designs and implements a solution, and rigorously evaluates it against defined criteria — contributing original analytical insight.
A good dissertation problem:
- Is grounded in a real domain or workplace context.
- Has a specific constraint or condition that introduces genuine complexity.
- Admits multiple possible solutions worth comparing or evaluating.
- Can be scoped and completed within one semester with measurable outcomes.
Process
| Stage | Description | Plagiarism Limit | Duration |
|---|---|---|---|
| Outline / Abstract Review | Submit outline proposal on the Dissertation Management System. Registration subject to eligibility verification by the academic team. Outline is reviewed and approved by the BITS Evaluator. | — | 10 minutes |
| Mid-Semester Progress Review | Present progress report, demonstration, and interim findings to the supervisor and BITS Evaluator. Plagiarism check via Turnitin required before submission. | 25% | 15 minutes |
| Final Evaluation | Submit final dissertation report with comprehensive presentation and demonstration. Final report must be submitted to supervisor at least two weeks before the deadline. Viva conducted by the BITS Evaluator via MS Teams. | 20% | 30 minutes (20 min presentation + 10 min viva) |
People Involved
| Role | Qualification | Responsibility |
|---|---|---|
| Supervisor | B.E./B.Tech/M.Sc/MBA/MCA with minimum 5 years of relevant experience | Guides the student through problem scoping, solution design, and report writing. Must be from the same organisation. |
| Additional Examiner | M.E./M.Tech/MS/MBA/PhD | Provides an independent evaluation of the dissertation. Must be from the same organisation. |
| BITS Evaluator | Assigned by BITS Pilani | Provides academic oversight, reviews the outline, assesses mid-semester progress, and conducts the final Viva. All interactions via MS Teams. |
Writing the Dissertation
The dissertation report is a formal academic document that must adhere to the prescribed format available on the Dissertation Management System. It should be written with clarity, precision, and academic rigour. The following sections are typically expected:
| Section | Description |
|---|---|
| Introduction | Context, motivation, and significance of the problem. |
| Literature Review | Survey of existing work, techniques, and approaches relevant to the problem. |
| Problem Statement | A precise articulation of the problem, its specific challenge or constraint, and measurable outcomes that define success. |
| Objectives, Scope & Constraints | Specific research objectives, boundaries of the work, and constraints under which the solution must operate. |
| Methodology | The approach taken, design decisions, and justification for the chosen solution. |
| Implementation | Description of the system or solution built, tools, datasets, and experimental setup. |
| Results & Analysis | Presentation and critical analysis of findings, with appropriate metrics and evaluation criteria. |
| Conclusions & Future Work | Summary of contributions, limitations, and directions for further investigation. |
Illustrative Example: Problem Statement
The following is an illustrative example of a well-scoped dissertation problem statement. It demonstrates how a commonly attempted problem can be elevated from a routine implementation to a dissertation-worthy applied research contribution through precise scoping, constraint identification, and measurable outcomes.
Domain: Enterprise Knowledge Management
Problem: Large organisations maintain extensive internal documentation — policies, technical manuals, and process guidelines — that employees struggle to navigate efficiently. Existing keyword-based search systems return irrelevant results and fail to answer contextual queries.
Specific Challenge: General-purpose Large Language Models (LLMs), when applied directly to this problem, hallucinate responses and cannot reliably ground answers in organisation-specific documentation. Retrieval Augmented Generation (RAG) is a promising approach, but naive RAG implementations suffer from poor retrieval precision when documents are lengthy, structurally inconsistent, or use domain-specific terminology.
Proposed Approach: Design and evaluate a RAG pipeline with domain-adapted chunking, embedding, and retrieval strategies optimised for the characteristics of enterprise documentation.
Measurable Outcomes:
- Retrieval precision and recall against a manually curated ground truth query set.
- Answer faithfulness score measured against source documents.
- Comparison against a baseline naive RAG implementation and keyword search.
- Response latency within acceptable bounds for interactive use.
Scope: Limited to a specific document corpus of defined size and type. Evaluation restricted to a defined query set. Model fine-tuning is out of scope.
Constraints: Solution must operate within available cloud compute budget. Proprietary or confidential documents must be handled in compliance with organisational data policies.
Suggested Research Areas
Conversational AI & ChatbotsQuestion Answering SystemsMachine Translation (including Indic Languages)Sentiment & Opinion AnalysisInformation ExtractionAutomatic SummarizationFake News & Cyberbullying DetectionPlagiarism DetectionFine-tuning Small Language ModelsRetrieval Augmented Generation (RAG)Social Media Monitoring & AnalyticsPersonalised Recommendations using NLP
Agentic AI & Autonomous SystemsExplainable AI for FintechFederated Learning for HealthcareGraph Neural Networks for Fraud DetectionReinforcement Learning for RoboticsMLOps for Continuous DeploymentReal-time Analytics on Streaming DataScalable Feature Store DesignData Quality & Drift MonitoringGenerative AI for Smart CitiesArchitecting AI Systems
Image Analysis & Medical ImagingVideo Analysis & Activity RecognitionMultimodal Emotion RecognitionMultimodal Information Retrieval3D Reconstruction & Spatial PerceptionAudio Signal Processing & Speech RecognitionAutonomous Vehicle PerceptionComputational Photography ApplicationsAR/VR Scene UnderstandingMultimodal AI Systems
AI for Sustainable AgricultureClimate Change Data AnalysisPredictive Maintenance in ManufacturingTraffic Management & OptimisationDrug Discovery using MLPersonalised MedicineAlgorithmic Trading & Risk ManagementCybersecurity: Malware & Intrusion DetectionAI for Energy EfficiencyInventory Management & Demand Forecasting
Support and Guidance
Institute Support
- BITS Campus Lab Access is available to all dissertation students for computational and experimental needs.
- OpenAthens eLibrary Access provides access to academic journals, research papers, and reference materials to support literature review and research.
- The dissertation course handout, containing complete instructions and templates, is available on the elearn Taxila portal.
- The BITS Evaluator is assigned to every student to evaluate progress at each stage — outline, mid-semester, and final.
Organisation Mentor
- The Mentor assigned during admission can serve as the Supervisor for the dissertation.
- If the Mentor is not the Supervisor, the Mentor must be the Additional Examiner.
- The Supervisor and Additional Examiner must be two different individuals, preferably from the student's current employing organisation.
- The student, in consultation with the Supervisor, identifies the dissertation topic and prepares the detailed outline proposal.
- It is recommended that the Mentor and Additional Examiner participate in the Final Viva session. The session invite will be shared with them by BITS Pilani.
- If the Mentor and Additional Examiner are unavailable for the Final Viva, the faculty will proceed with the viva with the student alone.
Experiential Learning
The WILP–CSIS Lab Infrastructure is a comprehensive, cloud-enabled academic computing ecosystem designed to support large-scale teaching, research, and hands-on experimentation across Computer Science and Information Systems programmes. Supporting nearly 25,000 lab instances annually across 92 courses, it provides flexible, scalable, and high-performance lab environments that integrate seamlessly with the eLearn portal — combining cloud technologies, high-performance computing, GPU acceleration, distributed systems, and cloud-native services.
At a Glance
- Comprehensive lab ecosystem designed for CSIS students with seamless integration to the eLearn portal
- Supports approximately 25,000 student lab instances annually across 92 courses
- Infrastructure consists of 5 specialized lab environments catering to diverse academic and research workloads
- Accelerated by enterprise-grade NVIDIA GPUs delivering AI-scale compute performance
- Supports teraFLOPS-scale AI and deep learning workloads using GPU-accelerated infrastructure
- Combines multi-core CPU infrastructure with high-performance GPU acceleration for AI/ML research
- Provides 24/7 access to all major lab environments for flexible student learning
- All labs are supplemented with “lab capsules” — lab sheets, datasets, source code, demos, and supporting material
Virtual Lab
- Cloud-backed environment with dynamic resource allocation
- Supports multiple machine configurations based on course requirements
- Optimized for programming, software development, coursework, and experimentation
- Accessible directly through the eLearn portal
Cluster Lab
- Designed for Big Data and distributed computing experiments
- Supports multi-machine, cluster-based workloads
- Enables hundreds of concurrent users on shared infrastructure
- Supports technologies such as Hadoop, Spark, Kafka, and OpenMPI
Remote Lab (HPC — Hyderabad Campus)
- High-performance computing (HPC) environment hosted at the Hyderabad campus
- 42 high-end servers with hundreds of CPU cores and enterprise-grade storage
- Enterprise-grade GPU infrastructure including NVIDIA A100 and L40S GPUs
- Supports AI/ML workloads, deep learning, HPC research, and large-scale data processing
- Kubernetes- and Kubeflow-based GPU infrastructure for scalable execution
- High-capacity NAS and SAN storage systems for persistent workloads
- 8×A100 compute: ~156 TFLOPS FP32 and ~2.5 PFLOPS tensor performance (FP16/BF16)
Cloud Console Lab
- Restricted-access AWS learning environment
- Hands-on exposure to 21+ AWS services including EC2, EBS, S3, IAM, Lambda, RDS, EKS, and EMR
- Designed for cloud-native coursework and infrastructure learning
- Incorporates cost-control and restricted-access mechanisms for optimized cloud usage
Software & Tool Ecosystem
- 138 software tools across 9 technology categories (Data Science & AI, DevOps, Databases, Big Data, Networking & Security, Robotics, Programming, Operating Systems, and Utilities)
- AI/ML frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, OpenCV, Jupyter Notebook, Pandas, and MLflow
- DevOps & cloud-native: Docker, Kubernetes, Jenkins, and Airflow
- Big Data ecosystem: Hadoop, Hive, Spark, Kafka, and Sqoop
- Programming platforms: Java, Python, Node.js, React, Django, and Spring Boot
- Databases & utilities: PostgreSQL, MongoDB, and Wireshark
- Covers networking, cybersecurity, robotics, simulation, databases, and operating systems
Infrastructure Expansion (2026–27)
- Planned GPU expansion with RTX PRO 6000 Blackwell GPU servers
- Expansion of GPU cluster infrastructure with additional high-end GPUs
- New container-based GPU Lab using Runpod.io for AI/ML workloads
- Faster deployment, reduced startup time, and optimized GPU utilization through containerization
- Designed to support over 5,000 users with scalable GPU access
- Introduction of analytics dashboards for monitoring and operational insights
- Redesign of cluster architecture with high-availability controller nodes and segregated CPU/GPU pools
- Integration of Longhorn distributed storage for redundancy and high availability
Programme Highlights
- Fully integrated with the eLearn platform
- 24/7 globally accessible infrastructure
- Scalable from individual notebook execution to thousands of concurrent users
- Cost optimization through dynamic allocation, restricted cloud access, and container-based GPU delivery
- Industry-aligned infrastructure supporting modern AI, cloud computing, HPC, and distributed systems education
Academic Processes
This section covers the key processes and references you need to navigate the programme — from learning platforms and session schedules to registration, grading, and contacts. Examination schedules, academic calendars, and hall tickets will be communicated by the support team via the elearn portal. For complete programme policies and academic regulations, refer to the BITS Pilani WILP Academic Regulations.
Learning Platforms
The programme uses two interconnected platforms. Understanding what lives where will help you navigate efficiently.
elearn Portal — elearn.bits-pilani.ac.in
The elearn portal is the programme-level gateway managed by the operations team. It contains:
- Programme announcements and important notices
- Academic calendar and important dates
- Links to Taxila LMS course pages
- Links to the Dissertation Management System
- Links to virtual labs and BITS digital library
- Exam-related communications and hall tickets
Taxila LMS — Course Pages
Each course has its own dedicated page within Taxila LMS, managed by the Professor/Instructor in charge. The course page contains:
- Course handout and slide decks
- EC1 Proforma — schedule of quizzes, assignments, and mini-projects
- Lab materials (accessible via the Labware option)
- Discussion forums monitored by the Learning Facilitator
- Previous year question papers
- Contact details of Professor/Instructor in charge, Instructors, and Learning Facilitators
- Session recordings will be available in MS Teams
Grouping of Courses
Electives in each semester are organised into groups. Students can choose only one course per group.
Semester II Groups
| Group | Courses |
|---|---|
| 1 | Natural Language Processing |
| 2 | Computer Vision, AI and ML for Cyber Security, Software Engineering for ML |
| 3 | Unsupervised Deep Learning, Video Analysis, Probabilistic Graphical Models |
| 4 | Information Retrieval, Distributed Machine Learning, Data Management for ML |
Semester III Groups
| Group | Courses |
|---|---|
| 1 | Audio Analysis, Speech Processing, Fair Interpretable Trustworthy ML |
| 2 | NLP Applications, Computational Learning Theory, Computational Photography |
| 3 | 3D Computer Vision, Machine Learning on the Edge, API Driven Cloud Native Solutions |
| 4 | Conversational AI, Distributed Machine Learning, Quantum ML, Multimodal IR |
| 5 | Software Engineering for ML, Social Media Analytics, AI and ML for Robotics |
| 6 | LLM for Generative AI, Graph Neural Networks, MLOps |
Where to Find What
| What | Where |
|---|---|
| Course handout | Course page on Taxila LMS |
| Slide decks and lecture materials | Course page on Taxila LMS |
| Session recordings | MS Teams course channel — available after each session |
| EC1 schedule — quizzes, assignments, mini-projects | EC1 Proforma on course page in Taxila LMS |
| Previous year question papers | Course page on Taxila LMS |
| Lab materials | Taxila LMS — Labware option in the drop-down |
| Contact details of IC, Instructors, LF | Course page on Taxila LMS |
| Exam calendar and hall tickets | elearn portal — announcements section |
| Academic calendar and important dates | elearn portal — announcements section |
| Dissertation Management System | elearn portal |
| Virtual labs | elearn portal |
| BITS digital library (OpenAthens) | elearn portal |
| Programme announcements | elearn portal — announcements section |
Escalation Matrix
For any unresolved query, follow the escalation path below. Always begin with the first point of contact before escalating.
| Query Type | First Point of Contact | Escalation |
|---|---|---|
| Academic — course content, syllabus, assignments | Learning Facilitator (LF) via discussion forum on Taxila LMS | Professor/Instructor in charge (IC) |
| EC1 — quiz and assignment marks | Learning Facilitator (LF) | Professor/Instructor in charge (IC) |
| Registration and elective selection | registration@wilp.bits-pilani.ac.in | support@wilp.bits-pilani.ac.in |
| Examinations — centres, hall tickets, scheduling | support@wilp.bits-pilani.ac.in | — |
| Dissertation — proposal, supervisor, portal issues | project@wilp.bits-pilani.ac.in | — |
| Technical — portal access, login issues | support@wilp.bits-pilani.ac.in | — |
Contact for Queries
| Query Type | Contact |
|---|---|
| Specific course related | Professor/Instructor in charge (IC) / Learning Facilitator (LF) — refer course page on Taxila LMS |
| Quiz / Assignment related | Professor/Instructor in charge (IC) / Learning Facilitator (LF) — refer course page on Taxila LMS |
| Registration related | registration@wilp.bits-pilani.ac.in |
| Operations related | support@wilp.bits-pilani.ac.in |
| Exam related | support@wilp.bits-pilani.ac.in |
Frequently Asked Questions
General, academic, and operational queries.
