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M-TECH in Ai And Data Science at Indian Institute of Technology Jodhpur
Jodhpur, Rajasthan
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About the Specialization
What is AI and Data Science at Indian Institute of Technology Jodhpur Jodhpur?
This M.Tech AI and Data Science program at Indian Institute of Technology Jodhpur focuses on equipping students with advanced theoretical and practical knowledge in artificial intelligence and data science. It covers machine learning, deep learning, big data technologies, and optimization, crucial for driving India''''s digital transformation and addressing complex real-world challenges across various sectors.
Who Should Apply?
This program is ideal for engineering graduates (B.Tech/B.E.) in computer science, IT, electrical, electronics, or related fields, as well as M.Sc./MCA degree holders in relevant disciplines. It caters to fresh graduates seeking entry into high-demand AI/DS roles and working professionals aiming to upskill or transition into the rapidly evolving data-driven industry in India.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as Data Scientists, Machine Learning Engineers, AI Architects, or Research Scientists in India. The curriculum prepares them for roles in top Indian IT firms, startups, R&D centers, and global MNCs, with competitive starting salaries and significant growth trajectories in the dynamic Indian technology landscape.
Student Success Practices
Foundation Stage
Master Core AI/ML and Data Fundamentals- (Semester 1-2)
Dedicate significant effort to building a strong foundation in machine learning, deep learning, advanced data structures, and algorithms. Actively engage with course materials, solve problem sets, and use supplementary resources like NPTEL courses, Coursera specializations, and GeeksforGeeks to solidify understanding.
Tools & Resources
NPTEL, Coursera, GeeksforGeeks, Jupyter Notebooks, Python
Career Connection
A robust understanding of fundamentals is crucial for cracking technical interviews for Data Scientist or ML Engineer roles and forms the bedrock for advanced specialization.
Hands-on Project Development and Coding Practice- (Semester 1-2)
Beyond theoretical knowledge, focus on applying concepts through practical projects. Participate regularly in coding challenges on platforms like HackerRank, LeetCode, and Kaggle. Start building small-scale projects using Python and relevant AI/ML libraries to gain practical experience.
Tools & Resources
HackerRank, LeetCode, Kaggle, GitHub, Scikit-learn, TensorFlow, PyTorch
Career Connection
Practical project experience and strong coding skills are highly valued by recruiters and are essential for showcasing problem-solving abilities and building a strong portfolio for placements.
Engage in Peer Learning and Academic Discussions- (Semester 1-2)
Form study groups with peers to discuss complex topics, prepare for exams, and collaborate on assignments. Actively participate in departmental seminars, workshops, and guest lectures to broaden your perspective and network with faculty and senior students.
Tools & Resources
Group study sessions, Departmental forums, Academic clubs
Career Connection
Collaborative learning enhances problem-solving skills, provides diverse insights, and strengthens communication abilities, all vital for teamwork in professional AI/DS environments.
Intermediate Stage
Strategic Elective Selection and Major Project Initiation- (Semester 3)
Carefully choose program electives that align with your specific career interests, such as Natural Language Processing, Computer Vision, or Big Data Analytics. Begin your Major Project Part 1 by identifying a challenging problem, conducting a thorough literature review, and designing a robust methodology.
Tools & Resources
Research papers, Academic journals, Project management tools, Domain-specific libraries
Career Connection
Specialized electives provide in-depth expertise, while a strong Major Project demonstrates your ability to conduct independent research and apply knowledge to real-world problems, making you a competitive candidate.
Seek Industry Internships and Networking Opportunities- (Semester 3)
Actively pursue summer or semester-long internships with Indian tech companies, startups, or research labs working in AI/DS. Attend industry conferences, workshops, and career fairs to network with professionals and gain insights into industry trends and job market requirements.
Tools & Resources
LinkedIn, Internshala, Naukri.com, Industry events
Career Connection
Internships provide invaluable real-world experience, practical skill development, and potential pre-placement offers. Networking can open doors to mentorship and future job opportunities.
Cultivate Research and Critical Analysis Skills- (Semester 3)
Regularly read and critically analyze recent research papers and articles in your chosen AI/DS sub-domains. Practice summarizing findings, identifying strengths and weaknesses, and proposing extensions or alternative approaches to enhance your research aptitude and analytical thinking.
Tools & Resources
arXiv, Google Scholar, IEEE Xplore, ACM Digital Library
Career Connection
Strong research skills are crucial for roles in R&D, advanced ML engineering, and pursuing higher studies. Critical analysis helps in staying updated and innovating within the field.
Advanced Stage
High-Impact Major Project Completion and Documentation- (Semester 4)
Focus on completing your Major Project Part 2 with a significant, verifiable contribution. Document your work meticulously in a well-structured thesis, emphasizing the problem, methodology, results, and implications. Aim for a potential publication or patent filing based on your project outcomes.
Tools & Resources
LaTeX, Overleaf, Mendeley/Zotero, IITJ Thesis Guidelines
Career Connection
A high-quality Major Project is a powerful differentiator, demonstrating your expertise and research capabilities to prospective employers and facilitating entry into research-focused careers.
Intensive Placement Preparation and Mock Interviews- (Semester 4)
Engage in rigorous placement preparation, including mock technical and HR interviews. Practice solving algorithmic problems, discussing project experiences, and articulating your understanding of core AI/DS concepts. Tailor your resume and cover letters for specific job roles and companies.
Tools & Resources
InterviewBit, GeeksforGeeks Interview Corner, IITJ Career Development Cell
Career Connection
Thorough preparation significantly increases your chances of securing placements in leading technology companies, helping you land your desired role with a competitive salary package in India.
Continuous Learning and Open-Source Contribution- (undefined)
Commit to lifelong learning by staying updated with the latest advancements in AI and Data Science through online courses, blogs, and industry reports. Contribute to open-source projects to refine your coding skills, collaborate with global developers, and build a strong professional presence on platforms like GitHub.
Tools & Resources
GitHub, Medium, Analytics Vidhya, Kaggle Learn
Career Connection
Demonstrates proactive skill development and a passion for the field, enhancing your appeal to employers and fostering long-term career growth in the dynamic AI/DS industry.
Program Structure and Curriculum
Eligibility:
- B.Tech./B.E./AMIE in Computer Science and Engineering/Information Technology/Electrical Engineering/Electronics and Communication Engineering/Instrumentation Engineering/Chemical Engineering/Mechanical Engineering/Civil Engineering/Production Engineering or related disciplines. M.Sc./MCA in Computer Science/Information Technology/Mathematics/Statistics/Electronics/Physics/Computational Science or related disciplines. Valid GATE score in CS, EC, EE, ME, IN, PH, MA, ST. GATE requirement waived for IIT B.Tech. graduates with CGPA 8.0 or above. Reservation as per GoI norms.
Duration: 2 years (4 semesters)
Credits: 86 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AAS5010 | Introduction to AI and Machine Learning | Core | 6 | Introduction to AI, Problem Solving by Search, Knowledge Representation, Machine Learning Fundamentals, Supervised and Unsupervised Learning, Reinforcement Learning |
| AAS5020 | Data Science and Engineering | Core | 6 | Data Wrangling and Exploration, Data Visualization, Data Warehousing Concepts, Big Data Technologies (Hadoop, Spark), Data Governance, Scalable Data Processing |
| AAS5030 | Advanced Data Structures and Algorithms | Core | 6 | Advanced Data Structures (Heaps, Trees, Graphs), Sorting and Searching Algorithms, Dynamic Programming, Greedy Algorithms, Graph Algorithms, Complexity Analysis |
| AAS5040 | Deep Learning | Core | 6 | Neural Network Architectures, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Models and Deep Learning Frameworks |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AAS5050 | Optimization Methods for AI and Data Science | Core | 6 | Convex Optimization, Linear Programming, Gradient Descent and Variants, Stochastic Optimization, Lagrangian Duality, Optimization for Machine Learning |
| AAS5060 | Research Methodology | Core | 2 | Research Problem Formulation, Literature Review, Research Design, Data Collection and Analysis, Report Writing, Ethical Considerations in Research |
| PE1 | Program Elective 1 | Elective | 6 | |
| PE2 | Program Elective 2 | Elective | 6 | |
| AAS5070 | Program Elective Option: Advanced Topics in Machine Learning | Elective | 6 | Ensemble Methods (Boosting, Bagging), Random Forests, Support Vector Machines, Kernel Methods, Gaussian Processes, Bayesian Learning |
| AAS5080 | Program Elective Option: Natural Language Processing | Elective | 6 | Text Preprocessing and Language Models, Word Embeddings, Recurrent Neural Networks for NLP, Transformers (BERT, GPT), Sequence-to-Sequence Models, Text Classification and Named Entity Recognition |
| AAS5090 | Program Elective Option: Computer Vision | Elective | 6 | Image Processing Basics, Feature Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision, Object Detection and Image Generation |
| AAS5100 | Program Elective Option: Reinforcement Learning | Elective | 6 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Q-Learning and Policy Gradient, Deep Reinforcement Learning |
| AAS5110 | Program Elective Option: Time Series Analysis and Forecasting | Elective | 6 | Time Series Components, ARIMA and ARCH/GARCH Models, State Space Models, Exponential Smoothing, Prophet, Deep Learning for Time Series |
| AAS5120 | Program Elective Option: Big Data Analytics | Elective | 6 | Hadoop Ecosystem (Spark, MapReduce), HDFS, Stream Processing, NoSQL Databases, Data Lake Architectures, Data Governance for Big Data |
| AAS5130 | Program Elective Option: Cloud Computing for Data Science | Elective | 6 | Cloud Architectures (IaaS, PaaS, SaaS), AWS/Azure/GCP Services for Data Science, Serverless Computing, Containerization (Docker, Kubernetes), Cloud Security |
| AAS5140 | Program Elective Option: Graph Neural Networks | Elective | 6 | Graph Theory Basics, Graph Embeddings, Graph Convolutional Networks, Graph Attention Networks, Spectral Graph Theory, Applications in Social Networks |
| AAS5150 | Program Elective Option: Federated Learning | Elective | 6 | Privacy-Preserving AI, Distributed Learning Architectures, Homomorphic Encryption, Secure Multi-Party Computation, Differential Privacy, Federated Averaging |
| AAS5160 | Program Elective Option: Explainable AI (XAI) | Elective | 6 | Interpretability vs Explainability, LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), Feature Importance, Attention Mechanisms, Model Debugging |
| AAS5170 | Program Elective Option: Ethics in AI and Data Science | Elective | 6 | Algorithmic Bias and Fairness, Accountability and Transparency, Privacy Concerns in AI, Data Governance, Societal Impact of AI, Ethical AI Frameworks |
| AAS5180 | Program Elective Option: IoT and Edge AI | Elective | 6 | IoT Architectures and Sensor Networks, Edge Computing Principles, TinyML and On-device AI, Data Processing at the Edge, Security and Privacy in IoT, Cloud-Edge Integration |
| AAS5190 | Program Elective Option: Digital Image and Video Processing | Elective | 6 | Image Enhancement and Restoration, Image Segmentation, Feature Extraction, Video Compression, Motion Estimation, Object Tracking |
| AAS5200 | Program Elective Option: Cyber-Physical Systems | Elective | 6 | CPS Architectures and Embedded Systems, Real-time Operating Systems, Sensor Actuator Networks, Control Systems, Security in CPS, Smart Grids and Autonomous Systems |
| AAS5210 | Program Elective Option: Human-Computer Interaction | Elective | 6 | HCI Principles and User-Centered Design, Usability Engineering, User Interface Design, Evaluation Methods, Affective Computing, Virtual and Augmented Reality |
| AAS5220 | Program Elective Option: Computer Architecture and Parallel Processing | Elective | 6 | Processor Architectures (Pipelining, Cache), Parallel Computing Models, Multicore Processors, GPU Computing, Distributed Systems, Performance Optimization |
| AAS5230 | Program Elective Option: Blockchain Technology | Elective | 6 | Cryptography Basics, Distributed Ledger Technology, Consensus Mechanisms, Smart Contracts, Decentralized Applications, Blockchain Platforms and Scalability |
| AAS5240 | Program Elective Option: Biometrics | Elective | 6 | Biometric Modalities, Fingerprint, Face, Iris, Voice Recognition, Multimodal Biometrics, Performance Evaluation, Security and Privacy in Biometrics |
| AAS5250 | Program Elective Option: Optimization Theory | Elective | 6 | Linear Programming, Non-linear and Convex Optimization, Duality Theory, Integer Programming, Dynamic Programming, Metaheuristics and Network Optimization |
| AAS5260 | Program Elective Option: Quantum Computing for AI | Elective | 6 | Quantum Mechanics Basics, Qubits, Superposition, Entanglement, Quantum Gates, Quantum Algorithms (Shor, Grover), Quantum Machine Learning, Quantum Annealing |
| AAS6010 | Program Elective Option: Advanced Database Systems | Elective | 6 | Distributed and NoSQL Databases, Graph Databases, Object-Relational Databases, Data Stream Management, Database Security, Transaction Management |
| AAS6020 | Program Elective Option: Data Visualization | Elective | 6 | Principles of Visualization, Visual Perception, Data Storytelling, Interactive Dashboards, Visualization Tools (Tableau, PowerBI), High-Dimensional Data Visualization |
| AAS6030 | Program Elective Option: Spatial Data Science | Elective | 6 | Geographic Information Systems (GIS), Spatial Data Models, Geocoding, Spatial Analysis Techniques, Remote Sensing Data, Satellite Image Processing |
| CSP5010 | Program Elective Option: Advanced Operating Systems | Elective | 6 | Distributed and Network OS, Real-time Operating Systems, Virtualization and Cloud OS, OS Security, File Systems, Concurrency Control |
| CSP5030 | Program Elective Option: Advanced Computer Networks | Elective | 6 | Network Protocols, Software Defined Networking, Network Security, Wireless Networks, Internet of Things Networking, Quality of Service |
| CSP5040 | Program Elective Option: Image Processing | Elective | 6 | Image Enhancement and Restoration, Image Segmentation, Feature Extraction, Morphological Operations, Color Image Processing, Wavelet Transforms |
| CSP5050 | Program Elective Option: Information Security | Elective | 6 | Cryptography, Network Security, Cyber Forensics, Web and OS Security, Malware Analysis, Risk Management and Security Policies |
| CSP5060 | Program Elective Option: Distributed Systems | Elective | 6 | Distributed Architectures, Consensus Algorithms, Distributed Transactions, Message Passing, Remote Procedure Call, Fault Tolerance and Distributed File Systems |
| CSP5070 | Program Elective Option: Software Engineering | Elective | 6 | Software Development Life Cycle, Agile Methodologies, Software Design Patterns, Testing and Quality Assurance, Project Management, DevOps |
| EEP5010 | Program Elective Option: Communication Engineering | Elective | 6 | Digital Modulation, Channel Coding, Wireless Communication, MIMO Systems, OFDM and Spread Spectrum, Satellite and Optical Communication |
| EEP5020 | Program Elective Option: Digital Signal Processing | Elective | 6 | Discrete Time Signals, Z-Transform, DFT and FFT, Digital Filter Design, Adaptive Filters, Multi-rate Signal Processing |
| EEP5030 | Program Elective Option: Embedded Systems | Elective | 6 | Microcontrollers and RTOS, Sensor Interfacing, Embedded C, Device Drivers, IoT Devices, Power Management |
| MMP5010 | Program Elective Option: Data Warehousing and Data Mining | Elective | 6 | Data Warehousing Concepts, ETL Process and OLAP, Data Preprocessing, Association Rule Mining, Classification and Clustering, Predictive Modeling |
| MMP5020 | Program Elective Option: Business Analytics | Elective | 6 | Business Intelligence, Descriptive, Predictive, Prescriptive Analytics, Data-driven Decision Making, Customer Analytics, Marketing Analytics, Financial Analytics |
| HSP5010 | Program Elective Option: Managerial Economics | Elective | 6 | Demand and Supply Analysis, Production Theory, Cost Analysis, Market Structures, Pricing Strategies, Investment Decisions |
| HSP5020 | Program Elective Option: Financial Management | Elective | 6 | Financial Markets, Capital Budgeting, Working Capital Management, Cost of Capital, Capital Structure, Dividend Policy |
| AAS6990 | Program Elective Option: Directed Research 1 | Elective (Research) | 6 | Literature Review, Research Problem Formulation, Methodology Development, Preliminary Experimentation, Report Writing, Presentation of Findings |
| AAS6991 | Program Elective Option: Directed Research 2 | Elective (Research) | 6 | Advanced Research Topics, In-depth Study and Novel Contribution, Experimental Design, Data Analysis, Scientific Writing, Publication Preparation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AAS6000 | Major Project - Part 1 | Project | 12 | Project Formulation, Problem Definition, Literature Survey, Methodology Design, Initial Implementation/Experimentation, Project Report Part 1 |
| PE3 | Program Elective 3 | Elective | 6 | |
| PE4 | Program Elective 4 | Elective | 6 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AAS6000 | Major Project - Part 2 | Project | 18 | Advanced Implementation, Experimental Validation, Result Analysis and Interpretation, Thesis Writing, Oral Presentation, Project Defense |
