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M-TECH in Artificial Intelligence at Indian Institute of Technology Jodhpur
Jodhpur, Rajasthan
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About the Specialization
What is Artificial Intelligence at Indian Institute of Technology Jodhpur Jodhpur?
This Artificial Intelligence program at IIT Jodhpur focuses on equipping students with advanced theoretical knowledge and practical skills in AI, Machine Learning, and Deep Learning. It emphasizes fundamental concepts alongside cutting-edge applications, preparing graduates for the rapidly evolving AI landscape in the Indian industry. The program differentiates itself with a strong research focus and interdisciplinary approach, fostering innovation in intelligent systems.
Who Should Apply?
This program is ideal for engineering graduates, particularly from Computer Science, Information Technology, and related disciplines, seeking entry into core AI roles. It also caters to working professionals aiming to upskill in advanced AI methodologies or career changers transitioning into data science and machine learning roles within India''''s tech sector. A strong mathematical and programming background is a prerequisite for success in this demanding field.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative careers as AI Engineers, Machine Learning Scientists, Data Scientists, or Research Engineers across various sectors in India. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning upwards of INR 25-50 LPA. The program aligns with industry demands for skilled AI professionals, fostering innovation and problem-solving capabilities crucial for driving India''''s digital transformation.
Student Success Practices
Foundation Stage
Master Core AI and Math Fundamentals- (Semester 1-2)
Dedicate significant effort to thoroughly understand the foundational courses like Machine Learning, Deep Learning, Data Structures, Algorithms, and Mathematical Foundations for AI. Actively solve problems, review concepts regularly, and ensure a strong grasp of underlying principles. This rigorous approach builds the intellectual bedrock for advanced AI studies.
Tools & Resources
NPTEL lectures, GeeksforGeeks, HackerRank, LeetCode, MIT OpenCourseware, Standard textbooks (e.g., Bishop''''s PRML, Goodfellow''''s Deep Learning)
Career Connection
A robust foundation is critical for excelling in advanced subjects, cracking technical interviews at top firms, and developing strong problem-solving skills essential for any AI role, ensuring long-term career resilience.
Hands-on Lab Implementation & Projects- (Semester 1-2)
Actively participate in all lab sessions (ML Lab, DL Lab, RL Lab, NLP Lab). Beyond assignments, take initiative to build small personal projects using Python, TensorFlow/PyTorch, and scikit-learn. Experiment with different datasets and models to solidify understanding and explore practical applications of theoretical knowledge.
Tools & Resources
Kaggle datasets, GitHub, Google Colab, Jupyter Notebooks, Stack Overflow, Official documentation for ML frameworks
Career Connection
Practical experience and a portfolio of projects are vital for showcasing skills to potential employers, especially in the competitive Indian tech job market, differentiating candidates from their peers.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and collaboratively solve problems. Explaining concepts to others deepens your own understanding and helps identify knowledge gaps. This fosters a collaborative spirit and improves communication skills, crucial for teamwork in industry.
Tools & Resources
WhatsApp/Telegram groups, Google Meet, Whiteboard discussions, University library study rooms, Online forums like Reddit''''s r/MachineLearning
Career Connection
Enhances communication and teamwork skills, fosters a collaborative mindset, and creates a support network that can be beneficial for academic success and future professional networking within the AI community.
Intermediate Stage
Specialized Elective Exploration & Research- (Semester 2-3)
Strategically choose electives that align with your career interests (e.g., NLP, Computer Vision, Reinforcement Learning, Explainable AI). Delve deep into these chosen areas, reading research papers and attempting mini-projects or review papers related to these advanced topics to build niche expertise.
Tools & Resources
ArXiv, Google Scholar, IEEE Xplore, ACM Digital Library, Dedicated conferences (NeurIPS, ICML, CVPR, ACL)
Career Connection
Develops expertise in a niche AI domain, which is highly valued by specialized companies and research labs in India, opening doors to advanced roles and potential PhD opportunities.
Seek Industry Internships & Collaborations- (Semester 2-3)
Actively search for and pursue summer or semester-long internships in AI/ML roles at tech companies, startups, or research organizations. Leverage university career services and faculty connections. Work on real-world problems and contribute to ongoing projects to gain invaluable practical experience.
Tools & Resources
LinkedIn, Internshala, University placement portal, Faculty network, Company career pages, AngelList
Career Connection
Gains invaluable industry exposure, builds professional networks, applies academic knowledge to practical scenarios, and often leads to pre-placement offers (PPOs) in India, jumpstarting your career.
Participate in Competitions and Hackathons- (Semester 2-3)
Regularly participate in online coding competitions (CodeChef, HackerRank) and AI/ML hackathons (Kaggle competitions, university-level hackathons). These provide exposure to diverse problems and pressure test your skills, refining your problem-solving abilities under time constraints.
Tools & Resources
Kaggle, HackerEarth, Devfolio, ML competitions organized by companies like Google, Microsoft, Amazon in India
Career Connection
Develops fast problem-solving, teamwork, and project delivery skills under time constraints, which are highly attractive to recruiters, and allows for practical skill validation in a competitive setting.
Advanced Stage
M.Tech Dissertation & Research Publication- (Semester 3-4)
Focus intensively on your M.Tech dissertation (Part I & II). Choose a challenging research problem, conduct thorough literature review, design and implement novel solutions, analyze results, and aim for publication in a reputable conference or journal. This showcases advanced research capabilities.
Tools & Resources
LaTeX, Mendeley/Zotero, Git, Collaboration tools, University research labs, Faculty guidance and mentorship
Career Connection
Demonstrates independent research capabilities, deep domain expertise, and contributes to academic knowledge, highly beneficial for research roles, R&D positions, or pursuing a PhD, both in India and globally.
Advanced Skill Refinement & Interview Preparation- (Semester 3-4)
Refine your skills in specialized AI areas. Practice coding and machine learning concept questions extensively. Conduct mock interviews, focus on system design for AI applications, and prepare a strong resume and online portfolio that effectively highlights your projects and achievements.
Tools & Resources
LeetCode (Hard), InterviewBit, Glassdoor, Technical blogs, Mock interview platforms, Career counsellors, particularly those specializing in tech roles
Career Connection
Essential for cracking interviews at top-tier companies, securing high-paying placements, and demonstrating readiness for complex engineering challenges in the AI field, ensuring a successful transition into the industry.
Build a Strong Professional Network- (Semester 3-4)
Attend industry workshops, seminars, and conferences (e.g., Data Science Congress, India AI Summit). Network with professionals, researchers, and alumni in the AI domain. Actively engage on platforms like LinkedIn to build connections and stay updated on industry trends and job opportunities.
Tools & Resources
LinkedIn, Conference platforms, Alumni networks, Professional meetups (e.g., PyData meetups, AI/ML groups)
Career Connection
Opens doors to unexpected opportunities, mentorship, industry insights, and long-term career growth in the dynamic Indian AI ecosystem, providing a competitive edge in job searches and career progression.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 2 years (4 semesters)
Credits: 64 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSI5010 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning Algorithms, Unsupervised Learning Techniques, Ensemble Methods, Model Evaluation and Selection, Deep Learning Basics |
| CSI5020 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models (GANs, VAEs), Deep Reinforcement Learning |
| CSI5030 | Mathematical Foundations for AI | Core | 4 | Linear Algebra for Machine Learning, Probability and Statistics, Optimization Techniques, Vector Calculus, Information Theory, Random Variables and Distributions |
| CSI5040 | Data Structures and Algorithms | Core | 4 | Asymptotic Analysis, Arrays and Linked Lists, Trees and Heaps, Graphs, Sorting and Searching Algorithms, Dynamic Programming |
| CSI5050 | Machine Learning Lab | Lab | 2 | Python for Machine Learning, Data Preprocessing and Feature Engineering, Implementing Supervised Learning Algorithms, Implementing Unsupervised Learning Algorithms, Model Evaluation and Hyperparameter Tuning, Data Visualization for ML |
| CSI5060 | Deep Learning Lab | Lab | 2 | TensorFlow/PyTorch Basics, Building Convolutional Neural Networks, Implementing Recurrent Neural Networks, Transfer Learning Techniques, Hyperparameter Optimization for Deep Models, Generative Adversarial Network Implementation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSI5070 | Reinforcement Learning | Core | 4 | Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning (Q-learning, SARSA), Policy Gradient Algorithms, Deep Reinforcement Learning Architectures |
| CSI5080 | Natural Language Processing | Core | 4 | Text Preprocessing and Tokenization, Language Models and N-grams, Word Embeddings (Word2Vec, GloVe), Sequence Models (RNNs, Transformers), Text Classification and Sentiment Analysis, Machine Translation |
| CSI5090 | Computer Vision | Core | 4 | Image Representation and Filtering, Feature Detection and Description, Image Segmentation, Object Recognition and Detection, Image Classification with CNNs, 3D Computer Vision |
| CSI5000 | Elective - I | Elective | 4 | Topics vary based on chosen elective from the available pool |
| CSI5100 | Reinforcement Learning Lab | Lab | 2 | OpenAI Gym Environments, Implementing Q-learning and SARSA, Policy Gradient Algorithm Implementation, Deep Q-Networks (DQN), Value Iteration and Policy Iteration, Experimentation with RL Agents |
| CSI5110 | Natural Language Processing Lab | Lab | 2 | NLTK and SpaCy Libraries, Text Classification Implementation, Named Entity Recognition, Sentiment Analysis Techniques, Building Chatbots and Dialogue Systems, Using Hugging Face Transformers |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSI6010 | Elective - II | Elective | 4 | Topics vary based on chosen elective from the available pool |
| CSI6020 | Elective - III | Elective | 4 | Topics vary based on chosen elective from the available pool |
| CSI6030 | M.Tech Dissertation Part - I | Project | 8 | Literature Review and Problem Identification, Research Proposal Development, Defining Research Objectives and Methodology, Preliminary Design and Experimentation, Data Collection and Initial Analysis, Mid-term Presentation and Report |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSI6040 | M.Tech Dissertation Part - II | Project | 16 | Advanced Research and Development, Implementation of Proposed Solution, Extensive Experimentation and Evaluation, Detailed Data Analysis and Interpretation, Thesis Writing and Documentation, Final Dissertation Defense |
Semester pool
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSI5120 | Advanced Database Systems | Elective | 4 | Relational Database Theory, Query Processing and Optimization, Transaction Management and Concurrency Control, Distributed and Parallel Databases, NoSQL Databases, Data Warehousing and OLAP |
| CSI5130 | Cloud Computing | Elective | 4 | Cloud Computing Architecture, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Storage and Networking, Security and Privacy in Cloud, Cloud Deployment Models |
| CSI5140 | Advanced Operating Systems | Elective | 4 | Distributed Operating Systems, Network Operating Systems, Real-Time Operating Systems, Mobile Operating Systems, Virtualization and Containerization, Operating System Security |
| CSI5150 | Software Engineering | Elective | 4 | Software Development Life Cycles, Requirements Engineering, Software Design Principles, Software Testing and Quality Assurance, Software Project Management, Agile Methodologies |
| CSI5160 | Internet of Things | Elective | 4 | IoT Architecture and Protocols, Sensors, Actuators, and Embedded Systems, Wireless Communication Technologies (ZigBee, LoRa), IoT Platforms and Cloud Integration, Data Analytics for IoT, Security and Privacy in IoT |
| CSI5170 | Compiler Design | Elective | 4 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Target Code Generation |
| CSI5180 | Computer Graphics | Elective | 4 | Graphics Primitives and Rasterization, 2D and 3D Transformations, Viewing and Projections, Clipping and Hidden Surface Removal, Illumination Models and Shading, Texture Mapping and Ray Tracing |
| CSI5190 | Digital Image Processing | Elective | 4 | Image Fundamentals and Sampling, Image Enhancement (Spatial and Frequency Domain), Image Restoration, Image Compression, Image Segmentation, Morphological Image Processing |
| CSI5200 | Advanced Computer Architecture | Elective | 4 | Pipelining and Instruction Level Parallelism (ILP), Memory Hierarchy and Cache Design, Multiprocessors and Cache Coherence, Vector Processors, Graphics Processing Units (GPUs), Interconnection Networks |
| CSI5210 | Cryptography and Network Security | Elective | 4 | Symmetric Key Cryptography (AES, DES), Asymmetric Key Cryptography (RSA, ECC), Hash Functions and Digital Signatures, Network Security Protocols (IPSec, SSL/TLS), Firewalls and Intrusion Detection Systems, Authentication and Access Control |
| CSI5220 | Information Retrieval | Elective | 4 | Boolean and Vector Space Models, Indexing and Term Weighting, Relevance Feedback and Query Expansion, Evaluation of IR Systems, Web Search and Link Analysis, Recommendation Systems |
| CSI5230 | Distributed Computing | Elective | 4 | Distributed System Models, Interprocess Communication, Remote Procedure Calls (RPC), Distributed File Systems, Distributed Synchronization and Consensus, Fault Tolerance and Replication |
| CSI5240 | High-Performance Computing | Elective | 4 | Parallel Computing Architectures, Shared Memory Programming (OpenMP), Distributed Memory Programming (MPI), GPU Programming (CUDA), Performance Analysis and Optimization, Parallel Algorithms |
| CSI5250 | Software Defined Networking | Elective | 4 | SDN Architecture and Principles, OpenFlow Protocol, SDN Controllers (POX, Floodlight, ONOS), Network Virtualization, SDN Applications and Use Cases, NFV (Network Function Virtualization) |
| CSI5260 | Wireless and Mobile Computing | Elective | 4 | Wireless Communication Fundamentals, Mobile IP and Ad-hoc Networks, Cellular Networks (2G, 3G, 4G, 5G), Mobile Computing Platforms, Location-Based Services, Mobile Security |
| CSI5270 | Data Mining | Elective | 4 | Data Preprocessing and Cleaning, Association Rule Mining, Classification Algorithms (Decision Trees, SVM), Clustering Algorithms (K-Means, DBSCAN), Anomaly and Outlier Detection, Web Mining and Text Mining |
| CSI5280 | Big Data Analytics | Elective | 4 | Big Data Ecosystem (Hadoop, Spark), Distributed File Systems (HDFS), MapReduce Programming Model, NoSQL Databases for Big Data, Stream Processing (Kafka, Flink), Machine Learning with Big Data |
| CSI5290 | Blockchain Technology | Elective | 4 | Cryptographic Primitives for Blockchain, Distributed Ledger Technology (DLT), Consensus Mechanisms (PoW, PoS), Smart Contracts and DApps, Blockchain Platforms (Ethereum, Hyperledger), Blockchain Use Cases and Challenges |
| CSI5300 | Computer Vision for Robotics | Elective | 4 | Robot Perception Systems, Visual Odometry and SLAM, Object Recognition for Robotic Manipulation, Scene Understanding and Depth Estimation, Robot Navigation and Path Planning, Human-Robot Interaction using Vision |
| CSI5310 | AI for Cyber Security | Elective | 4 | Machine Learning for Malware Detection, AI for Intrusion Detection Systems, Natural Language Processing for Security Analytics, Reinforcement Learning for Cyber Defense, Adversarial Machine Learning in Security, Threat Intelligence with AI |
| CSI5320 | Edge AI | Elective | 4 | Introduction to Edge Computing, TinyML and Efficient AI Models, Model Quantization and Pruning, Federated Learning at the Edge, Edge AI Hardware Accelerators, Applications of Edge AI |
| CSI5330 | Explainable AI | Elective | 4 | Interpretability vs. Explainability, Model-Agnostic Explanations (LIME, SHAP), Model-Specific Explanations, Local and Global Explanations, Feature Importance and Visualization, Counterfactual Explanations |
| CSI5340 | AI in Healthcare | Elective | 4 | Medical Image Analysis with AI, AI for Disease Diagnosis and Prediction, Natural Language Processing in Healthcare, Drug Discovery and Development, Personalized Medicine, Ethical and Regulatory Aspects of AI in Healthcare |
| CSI5350 | Quantum Machine Learning | Elective | 4 | Quantum Computing Fundamentals (Qubits, Gates), Quantum Algorithms for Machine Learning, Quantum Supervised and Unsupervised Learning, Quantum Neural Networks, Quantum Optimization Algorithms, Near-Term Quantum Devices for ML |
| CSI5360 | Trustworthy AI | Elective | 4 | AI Ethics and Principles, Fairness and Bias in AI Systems, Accountability and Transparency, Robustness and Adversarial Attacks, Privacy-Preserving AI (Differential Privacy, Homomorphic Encryption), Explainability and Interpretability in Trustworthy AI |
| CSI5370 | Intelligent Agents | Elective | 4 | Agent Architectures (Reactive, Deliberative), Rational Agents and Environments, Knowledge Representation for Agents, Automated Planning, Learning Agents, Belief-Desire-Intention (BDI) Agents |
| CSI5380 | Multi-agent Systems | Elective | 4 | Introduction to Multi-agent Systems, Agent Communication Languages, Coordination and Cooperation, Negotiation and Bargaining, Distributed Problem Solving, Game Theory in Multi-agent Systems |
| CSI5390 | Speech and Audio Processing | Elective | 4 | Speech Production and Perception, Digital Audio Basics, Feature Extraction for Speech (MFCCs), Automatic Speech Recognition (ASR), Speaker Recognition, Text-to-Speech Synthesis |
| CSI5400 | Robotics and Automation | Elective | 4 | Robot Kinematics and Dynamics, Robot Control Architectures, Sensors and Actuators in Robotics, Path Planning and Navigation, Robot Vision and Object Grasping, Industrial Automation and Control |
| CSI5410 | Affective Computing | Elective | 4 | Emotion Theories and Models, Multimodal Affect Sensing, Emotion Recognition (Speech, Face, Text), Affective User Interfaces, Emotional AI Applications, Ethics of Affective Computing |
| CSI5420 | Brain-Computer Interfaces | Elective | 4 | Neurophysiology and Brain Signals (EEG, ECoG), BCI Architectures and Components, Signal Processing for BCIs, Machine Learning for BCI Decoding, Applications of BCIs (Assistive Devices, Gaming), Ethical Considerations in BCIs |
| CSI5430 | Computational Social Science | Elective | 4 | Big Data for Social Research, Social Network Analysis, Agent-Based Modeling, Text Analysis for Social Data, Computational Demography, Ethical Issues in Computational Social Science |
| CSI5440 | Bio-inspired AI | Elective | 4 | Evolutionary Algorithms (Genetic Algorithms), Swarm Intelligence (PSO, ACO), Neural Networks as Bio-inspired Models, Artificial Immune Systems, Cellular Automata, Applications in Optimization and Learning |
| CSI5450 | Optimization for Machine Learning | Elective | 4 | Convex Optimization Fundamentals, Gradient Descent and Variants (SGD, Adam), Constrained Optimization, Lagrange Multipliers and KKT Conditions, Stochastic Optimization, Optimization for Deep Learning |
| CSI5460 | Game Theory for AI | Elective | 4 | Basic Concepts of Game Theory, Nash Equilibrium, Cooperative and Non-Cooperative Games, Repeated Games and Folk Theorem, Mechanism Design, Applications in Multi-agent Systems |
| CSI5470 | Probabilistic Graphical Models | Elective | 4 | Bayesian Networks, Markov Random Fields, Exact Inference Algorithms, Approximate Inference Methods (Sampling, Variational), Learning Parameters in PGMs, Structure Learning |
| CSI5480 | Advanced Optimization | Elective | 4 | Linear Programming, Non-linear Programming, Integer Programming, Convex and Non-convex Optimization, Global Optimization, Metaheuristics (Simulated Annealing, Genetic Algorithms) |
| CSI5490 | Deep Learning for Robotics | Elective | 4 | Perception for Robots (Vision, Lidar), Robot Control with Deep Learning, Reinforcement Learning for Robotic Control, Manipulation and Grasping, Human-Robot Interaction, Sim-to-Real Transfer Learning |
| CSI5500 | Advanced Natural Language Processing | Elective | 4 | Deep Learning Architectures for NLP (Transformers), Pre-trained Language Models (BERT, GPT), Question Answering Systems, Text Summarization, Dialogue Systems and Chatbots, Cross-Lingual NLP |
| CSI5510 | Adversarial Machine Learning | Elective | 4 | Adversarial Examples, Threat Models and Attack Types, Robustness Evaluation of ML Models, Adversarial Training, Defenses Against Adversarial Attacks, Ethical Implications of Adversarial AI |
| CSI5520 | Fairness, Accountability, and Transparency in AI | Elective | 4 | Ethical Principles in AI, Defining and Measuring Fairness, Bias Detection and Mitigation Techniques, Explainable AI for Transparency, Accountability Frameworks, Regulatory Landscape for AI Ethics |
