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B-TECH-M-TECH-DUAL-DEGREE 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 Dual Degree program at IIT Jodhpur provides a rigorous foundation in core AI principles alongside practical applications, addressing the rapidly growing demand for AI professionals in India. It uniquely blends B.Tech and M.Tech curricula to cultivate deep expertise, preparing students for leadership roles in cutting-edge AI research and industry-driven innovation within the dynamic Indian tech landscape.
Who Should Apply?
This program is ideal for bright 10+2 graduates with a strong aptitude for mathematics and computing, seeking to specialize early in AI. It also suits those with a B.Tech background desiring an integrated master''''s for advanced research or product development roles. Aspiring data scientists, machine learning engineers, and AI researchers in India will find this program''''s comprehensive approach highly beneficial.
Why Choose This Course?
Graduates of this program can expect to secure high-demand roles as AI engineers, data scientists, machine learning specialists, and AI researchers in top Indian and multinational companies. Starting salaries for freshers typically range from INR 10-25 LPA, with significant growth potential. The program aligns with certifications like AWS Certified Machine Learning and Google Professional ML Engineer, boosting career trajectories in India''''s booming AI sector.
Student Success Practices
Foundation Stage
Build a Strong Mathematical & Programming Core- (Semester 1-2)
Dedicate significant time to mastering Calculus, Linear Algebra, Discrete Mathematics, and fundamental programming concepts (Python, C++). These are the bedrock for advanced AI. Actively solve problems from textbooks and online platforms to solidify understanding.
Tools & Resources
NPTEL courses for Math fundamentals, HackerRank, LeetCode for coding practice, Khan Academy for conceptual clarity
Career Connection
A robust foundation is critical for understanding complex AI algorithms and excelling in technical interviews for core engineering and data science roles.
Engage in Interdisciplinary Design Thinking- (Semester 1-2)
Actively participate in the ''''Design and Innovation'''' course and seek opportunities for interdisciplinary projects. Learn to identify real-world problems and apply creative, analytical thinking beyond purely technical solutions.
Tools & Resources
IIT Jodhpur Design & Innovation Centre workshops, IDEO Design Thinking resources, Local hackathons
Career Connection
This develops crucial problem-solving and innovation skills, highly valued by product-focused companies and startups in India, helping you stand out beyond just coding prowess.
Cultivate Effective Communication Skills- (Semester 1-2)
Focus on improving English Communication through academic writing, presentations, and group discussions. Clear communication is vital for collaborating in teams, presenting research, and articulating ideas to diverse audiences.
Tools & Resources
Toastmasters International (if available at IITJ), Online grammar tools (Grammarly), Departmental debate/presentation clubs
Career Connection
Strong communication skills are essential for both technical and leadership roles, ensuring you can convey complex AI concepts to non-technical stakeholders in Indian companies.
Intermediate Stage
Master Core AI/ML Frameworks with Projects- (Semester 3-5)
Go beyond theoretical understanding of Machine Learning, Deep Learning, and NLP. Implement algorithms from scratch, then master industry-standard frameworks like TensorFlow, PyTorch, and Scikit-learn through dedicated projects. Contribute to open-source if possible.
Tools & Resources
Kaggle competitions, GitHub for project hosting, Documentation for TensorFlow, PyTorch, Hugging Face
Career Connection
Hands-on experience with these tools is a non-negotiable for machine learning engineer and data scientist roles in India, demonstrating immediate deployable skills.
Seek Early Research & Industry Exposure- (Semester 3-5)
Look for opportunities to work with faculty on research projects or pursue summer internships (even short ones) in AI-focused companies or research labs. This provides practical context and helps in networking.
Tools & Resources
Faculty research group pages, LinkedIn for internship searches, IIT Jodhpur career services for industry contacts
Career Connection
Early exposure is invaluable for refining career interests, building a strong resume, and gaining insights into the Indian AI industry''''s real-world challenges and demands, enhancing M.Tech thesis relevance.
Build a Robust Data Engineering Foundation- (Semester 4-5)
Complement AI/ML skills with Big Data analytics. Learn Hadoop, Spark, and cloud platforms like AWS/Azure/GCP. Understand data pipelines, warehousing, and distributed computing, as AI models rely heavily on scalable data infrastructure.
Tools & Resources
Online courses on Apache Spark, AWS/Azure/GCP free tier accounts and tutorials, NPTEL courses on Big Data
Career Connection
This dual skillset makes you highly attractive for roles requiring end-to-end AI system deployment, from data ingestion to model serving, a critical requirement in many Indian tech firms.
Advanced Stage
Deep Dive into Specialization & M.Tech Project- (Semester 7-10)
Utilize M.Tech core and elective slots to specialize in a niche area of AI (e.g., Computer Vision, NLP, Reinforcement Learning, Explainable AI). Dedicate substantial effort to your Masters Research Project, aiming for publication or significant industry impact.
Tools & Resources
arXiv for latest research papers, Conferences like CVPR, NeurIPS, ACL, Advisor mentorship and departmental research labs
Career Connection
A strong M.Tech project, especially if it leads to publication or a robust prototype, is your most powerful asset for advanced R&D roles, PhD admissions, or leadership positions in Indian AI companies.
Develop Ethical AI Awareness and Leadership- (Semester 7-10)
Actively engage with topics in AI Ethics and Society. Understand the socio-economic implications of AI in the Indian context, including bias, fairness, and accountability. Participate in discussions or workshops on responsible AI development.
Tools & Resources
AI Ethics forums and policy papers, Discussions with faculty working on responsible AI, Workshops on AI governance
Career Connection
Beyond technical skills, companies in India are increasingly valuing professionals who can navigate the ethical complexities of AI, making you a more holistic and responsible leader in the field.
Network Extensively and Prepare for Placements/Higher Studies- (Semester 8-10)
Attend industry talks, career fairs, and connect with alumni. Refine your resume, practice mock interviews, and prepare a strong portfolio of projects. For higher studies, focus on GRE/TOEFL and crafting compelling statements of purpose.
Tools & Resources
IIT Jodhpur Career Development Cell, LinkedIn for professional networking, Online interview preparation platforms (e.g., InterviewBit)
Career Connection
Strategic networking and diligent preparation are crucial for securing top-tier placements in Indian tech firms or gaining admission to leading global universities for further research and academic pursuits.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 5 years (10 semesters)
Credits: 197 (Calculated from semester-wise breakdown; official document states minimum 224) Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA101 | Calculus | Core | 3 | Functions of one variable, Limits and continuity, Differentiation and applications, Integration and applications, Sequences and Series |
| PH101 | Physics-I | Core | 3 | Classical Mechanics, Special Theory of Relativity, Oscillations and Waves, Wave Optics, Introduction to Electromagnetism |
| PH102 | Physics Lab-I | Lab | 1 | Experiments on mechanics, Experiments on properties of matter, Experiments on optics, Data analysis and error estimation |
| CS101 | Introduction to Programming | Core | 3 | Programming fundamentals, Data types, variables, and expressions, Control structures (conditionals, loops), Functions and modular programming, Basic algorithms and problem-solving |
| HS101 | English Communication | Core | 3 | Reading comprehension and analysis, Academic writing skills, Verbal communication and presentation, Grammar and vocabulary building, Group discussions and public speaking |
| ED101 | Engineering Graphics | Core | 2 | Orthographic projections, Sectional views, Isometric views, Development of surfaces, Introduction to CAD software |
| DE101 | Design and Innovation | Core | 2 | Design thinking process, Problem identification and definition, Ideation and brainstorming, Prototyping and testing, Presentation of design solutions |
| AI101 | Introduction to Artificial Intelligence | Core | 3 | History and philosophy of AI, Intelligent agents and environments, Problem-solving by searching, Heuristic search techniques, Knowledge representation and reasoning |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA102 | Linear Algebra | Core | 3 | Vectors and vector spaces, Matrices and determinants, System of linear equations, Eigenvalues and eigenvectors, Linear transformations |
| PH103 | Physics-II | Core | 3 | Electrostatics and Magnetostatics, Electromagnetic Induction, Maxwell''''s Equations, Introduction to Quantum Mechanics, Statistical Mechanics and Thermodynamics |
| ME101 | Introduction to Manufacturing Processes | Core | 3 | Casting processes, Forming processes, Machining processes, Joining processes, Additive manufacturing fundamentals |
| CS102 | Data Structures | Core | 3 | Arrays and linked lists, Stacks and queues, Trees and binary search trees, Graphs and graph traversal algorithms, Hashing and collision resolution |
| EE101 | Basic Electrical Engineering | Core | 3 | DC circuits and theorems, AC circuits and phasor analysis, Transformers and their operation, DC and AC machines, Basic power systems |
| CH101 | Chemistry | Core | 3 | Atomic structure and bonding theories, Chemical thermodynamics, Reaction kinetics, Electrochemistry, Organic chemistry fundamentals |
| CH102 | Chemistry Lab | Lab | 1 | Volumetric analysis experiments, Qualitative analysis of ions, Synthesis of inorganic/organic compounds, pH and conductivity measurements |
| BT101 | Introduction to Biological Sciences | Core | 3 | Cell structure and function, Genetics and molecular biology, Microbiology and immunology, Biotechnology and its applications, Ecology and environmental biology |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Discrete Mathematics | Core | 3 | Mathematical logic and proof techniques, Set theory and functions, Combinatorics and counting principles, Graph theory fundamentals, Recurrence relations |
| CS201 | Design and Analysis of Algorithms | Core | 3 | Algorithm analysis techniques (time, space complexity), Divide and conquer algorithms, Dynamic programming, Greedy algorithms, Graph algorithms (BFS, DFS, shortest paths) |
| AI201 | Probability and Statistics for AI | Core | 3 | Probability theory and distributions, Random variables and expectation, Hypothesis testing and estimation, Correlation and regression analysis, Bayesian inference |
| AI202 | Machine Learning Fundamentals | Core | 3 | Introduction to machine learning paradigms, Supervised learning (regression, classification), Unsupervised learning (clustering, dimensionality reduction), Model evaluation and validation, Feature engineering and selection |
| AI203 | Programming for AI | Core | 3 | Python programming for data science, NumPy and Pandas for data manipulation, Matplotlib and Seaborn for data visualization, Introduction to Scikit-learn, API integration and scripting for AI tasks |
| ES201 | Environmental Studies | Core | 2 | Ecosystems and biodiversity, Natural resources and conservation, Environmental pollution and management, Climate change and global warming, Sustainable development practices |
| HSEE | Humanities and Social Sciences Elective | Elective | 3 | Varies based on specific elective choice offered by the department |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA202 | Optimization Methods | Core | 3 | Linear programming and Simplex method, Non-linear programming, Convex optimization, Gradient descent and its variants, Constrained and unconstrained optimization |
| CS202 | Database Management Systems | Core | 3 | Relational model and algebra, Structured Query Language (SQL), Entity-Relationship (ER) modeling, Normalization theory, Transaction management and concurrency control |
| AI204 | Deep Learning | Core | 3 | Neural network architectures, Backpropagation algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Optimization techniques and regularization |
| AI205 | Computer Vision | Core | 3 | Image formation and perception, Image processing fundamentals, Feature extraction and matching, Object detection and recognition, Image segmentation and tracking |
| AI206 | Natural Language Processing | Core | 3 | Text preprocessing and tokenization, Word embeddings (Word2Vec, GloVe), Language models and sequence modeling, Machine translation, Sentiment analysis and text classification |
| AI207 | AI Lab-I | Lab | 2 | Implementation of machine learning algorithms, Deep learning framework exercises (TensorFlow, PyTorch), Computer vision applications, Natural language processing projects |
| HSEE | Humanities and Social Sciences Elective | Elective | 3 | Varies based on specific elective choice offered by the department |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Operating Systems | Core | 3 | Process management and scheduling, Memory management and virtual memory, File systems and I/O management, Concurrency and deadlocks, Operating system security |
| AI301 | Reinforcement Learning | Core | 3 | Markov Decision Processes (MDPs), Dynamic programming for RL, Monte Carlo methods, Temporal-difference learning (Q-learning, SARSA), Policy gradient methods |
| AI302 | Big Data Analytics | Core | 3 | Introduction to Big Data concepts, Distributed file systems (HDFS), MapReduce programming model, Apache Spark for data processing, Data warehousing and streaming analytics |
| AI303 | AI Ethics and Society | Core | 3 | Ethical principles for AI, Bias, fairness, and accountability in AI systems, Privacy and data protection in AI, Societal impact of AI (employment, surveillance), AI regulation and governance |
| AIEL | AI Elective-I | Elective | 3 | Varies based on specific elective choice offered by the department (e.g., Speech Processing, Robotics AI) |
| AI304 | AI Lab-II | Lab | 2 | Advanced machine learning projects, Deep reinforcement learning implementations, Big data processing with Spark, Ethical AI challenge solutions |
| OEL | Open Elective | Elective | 3 | Varies based on specific elective choice offered across departments |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS302 | Computer Networks | Core | 3 | Network models (OSI, TCP/IP), Data link layer protocols, Network layer protocols (IP, routing), Transport layer protocols (TCP, UDP), Network security fundamentals |
| AI305 | Advanced Machine Learning | Core | 3 | Ensemble methods (Bagging, Boosting), Bayesian machine learning, Kernel methods and SVMs, Dimensionality reduction techniques, Anomaly detection |
| AI306 | Probabilistic Graphical Models | Core | 3 | Bayesian networks, Markov Random Fields, Inference algorithms (exact and approximate), Learning parameters and structure, Applications in AI |
| AIEL | AI Elective-II | Elective | 3 | Varies based on specific elective choice offered by the department (e.g., Explainable AI, Generative Models) |
| OEL | Open Elective | Elective | 3 | Varies based on specific elective choice offered across departments |
| AI307 | Minor Project | Project | 4 | Project proposal and literature review, Design and implementation of a small AI system, Experimentation and result analysis, Technical report writing, Project presentation and demonstration |
| AI308 | AI Seminar | Seminar | 1 | Research paper presentation, Critical analysis of AI advancements, Public speaking and communication skills, Discussion on emerging AI trends |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIEL | AI Elective-III | Elective | 3 | Varies based on specific elective choice offered by the department |
| AIEL | AI Elective-IV | Elective | 3 | Varies based on specific elective choice offered by the department |
| MTEP | M.Tech Elective-I | Elective (M.Tech) | 3 | Varies based on specific M.Tech elective choice offered by the department |
| MTEP | M.Tech Elective-II | Elective (M.Tech) | 3 | Varies based on specific M.Tech elective choice offered by the department |
| OEL | Open Elective | Elective | 3 | Varies based on specific elective choice offered across departments |
| AIPJ | AI Project | Project | 6 | Advanced problem definition in AI, Literature survey and methodology selection, System design and implementation (Phase I), Initial results and progress reporting, Research ethics and intellectual property |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIEL | AI Elective-V | Elective | 3 | Varies based on specific elective choice offered by the department |
| AIEL | AI Elective-VI | Elective | 3 | Varies based on specific elective choice offered by the department |
| MTEP | M.Tech Elective-III | Elective (M.Tech) | 3 | Varies based on specific M.Tech elective choice offered by the department |
| MTEP | M.Tech Elective-IV | Elective (M.Tech) | 3 | Varies based on specific M.Tech elective choice offered by the department |
| AIPJ | AI Project | Project | 6 | Continuation of AI Project from Semester 7, Advanced experimentation and rigorous analysis, Thesis writing and documentation, Oral defense and presentation of findings, Contribution to research and innovation |
Semester 9
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTEP | M.Tech Elective-VII | Elective (M.Tech) | 3 | Varies based on specific M.Tech elective choice offered by the department |
| MTEP | M.Tech Elective-VIII | Elective (M.Tech) | 3 | Varies based on specific M.Tech elective choice offered by the department |
| AI602 | Masters Research Project Part-II | Project | 12 | Execution of research methodology, Advanced experimentation and results analysis, Interpretation of findings and drawing conclusions, Comprehensive thesis writing, Pre-submission defense and final thesis defense |
