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DUAL-DEGREE-B-TECH-BS-M-TECH-MS in Artificial Intelligence at Indian Institute of Technology Kharagpur
Paschim Medinipur, West Bengal
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About the Specialization
What is Artificial Intelligence at Indian Institute of Technology Kharagpur Paschim Medinipur?
This Artificial Intelligence Dual Degree program at IIT Kharagpur focuses on developing a deep understanding of AI principles and their applications, addressing the rapidly expanding demand for skilled AI professionals in the Indian market. It integrates foundational computer science with advanced machine learning, deep learning, and data analytics, preparing students for cutting-edge roles in the technology sector and equipping them with the expertise to innovate within India''''s growing digital economy. The curriculum emphasizes both theoretical knowledge and practical implementation to foster comprehensive AI expertise.
Who Should Apply?
This program is ideal for high-achieving fresh graduates who have excelled in the JEE Advanced exam, demonstrating strong aptitude in mathematics, physics, and computer science. It also caters to students eager to pursue a comprehensive five-year academic journey culminating in both undergraduate and postgraduate degrees in AI. Individuals passionate about solving complex problems, innovating with data, and contributing to India''''s technological advancements will thrive, with a prerequisite for strong analytical and problem-solving skills and a keen interest in advanced computing.
Why Choose This Course?
Graduates of this program can expect to secure highly sought-after roles such as AI Engineer, Machine Learning Scientist, Data Scientist, or Research Engineer within leading Indian and multinational companies. Entry-level salaries typically range from INR 10-25 LPA, with experienced professionals earning significantly higher based on expertise and role. Graduates are well-positioned for leadership roles in AI product development, research, and academia, contributing to India''''s technological self-reliance and global competitiveness, often leading to roles at firms like TCS, Wipro, Infosys, and various AI-focused startups.
Student Success Practices
Foundation Stage
Master Core Programming and Mathematics- (Semester 1-2)
Dedicate significant time to fundamental programming concepts (C/Python) and mathematical areas like Calculus, Linear Algebra, and Probability. Utilize platforms such as HackerRank and LeetCode for coding challenges, and NPTEL/Coursera for supplementary math courses. Collaborate actively with peers on problem sets to solidify understanding of basic principles.
Tools & Resources
HackerRank, LeetCode, NPTEL, Coursera
Career Connection
A strong foundation in these areas is crucial for success in advanced AI courses and serves as a primary filter for early-stage internships and placements in leading tech companies.
Engage in Academic Societies and Clubs- (Semester 1-2)
Actively participate in the Robotics Club, Programming & Data Science Society, or similar technical groups at IIT Kharagpur. These platforms provide hands-on project experience, networking opportunities with seniors and faculty, and exposure to practical problem-solving beyond the standard curriculum. Seek mentorship from senior students to navigate academic and technical challenges.
Tools & Resources
IIT KGP Student Clubs, Departmental Workshops
Career Connection
Early involvement demonstrates initiative and provides practical skills, significantly enhancing your profile for future internships and fostering a collaborative learning environment essential for professional growth.
Cultivate Effective Study Habits- (Semester 1-2)
Develop a disciplined study routine, ensure regular attendance in all lectures and tutorials, and actively participate in class discussions. Practice time management techniques, and don''''t hesitate to seek clarification and help from Teaching Assistants or professors during their office hours. Forming study groups to discuss complex topics and prepare for examinations is highly beneficial.
Tools & Resources
Academic Calendar, Lecture Notes, Study Groups
Career Connection
Consistent academic performance builds a strong GPA, which is a critical factor for competitive internships, higher education opportunities, and for shortlisting by companies during campus placements.
Intermediate Stage
Undertake AI/ML Projects and Kaggle Competitions- (Semester 3-5)
Apply theoretical knowledge gained from Machine Learning, Deep Learning, and NLP courses to real-world datasets. Participate in platforms like Kaggle or DrivenData competitions to hone practical implementation skills, build a strong project portfolio, and learn from diverse problem statements and community-contributed solutions. Focus on implementing models and algorithms from scratch to deepen understanding.
Tools & Resources
Kaggle, DrivenData, GitHub, PyTorch/TensorFlow
Career Connection
A robust project portfolio and significant competition experience are invaluable for showcasing practical AI skills to recruiters and securing coveted AI/ML internships in the Indian tech landscape.
Seek Mentorship and Industry Exposure- (Semester 3-5)
Connect proactively with faculty members engaged in AI research within the School of AI and actively seek opportunities to assist in their labs. Attend industry seminars, workshops, and guest lectures organized by the department or institute. Explore possibilities for summer research internships (SRI) at other IITs, IISc, or other reputed research institutions.
Tools & Resources
Departmental Notifications, LinkedIn, Faculty Research Pages
Career Connection
Mentorship provides crucial guidance for specialization paths, while industry exposure offers practical insights into real-world applications and significantly expands professional networks, which are crucial for placements and future career development.
Specialize in AI Sub-fields and Electives- (Semester 3-5)
Carefully select department and open electives that align with your emerging interests, such as Computer Vision, Reinforcement Learning, Robotics, or Natural Language Processing. Utilize online platforms like Coursera Specializations or edX MicroMasters programs to deepen your knowledge in chosen areas, complementing and extending classroom learning.
Tools & Resources
Coursera, edX, IIT KGP Course Catalog
Career Connection
Specialized knowledge makes you a more attractive candidate for specific AI roles and research positions, allowing you to target niche areas and advanced opportunities within the dynamic Indian tech industry.
Advanced Stage
Intensive Placement and Interview Preparation- (Semester 6-8)
Begin rigorous preparation for technical interviews by practicing data structures, algorithms, and system design problems extensively on platforms like InterviewBit and LeetCode. Focus specifically on AI/ML-related questions, case studies, and behavioral interviews relevant to top tech companies. Actively participate in mock interviews organized by the placement cell or student groups.
Tools & Resources
InterviewBit, LeetCode, GeeksforGeeks, IIT KGP Placement Cell
Career Connection
Thorough and dedicated preparation is essential for successfully navigating and cracking interviews at top-tier Indian tech companies and multinational corporations, leading to successful placements with competitive compensation packages.
Undertake a Substantial Dual Degree Project/Dissertation- (Semester 6-10)
Invest deeply in your M.Tech project/dissertation (Parts I & II) by aiming for innovative solutions, publishable research outcomes, or projects with significant industrial impact. Collaborate closely with faculty, industry mentors, and research groups. Your comprehensive project should clearly demonstrate advanced AI expertise, strong research acumen, and superior problem-solving capabilities.
Tools & Resources
Research Papers, Academic Journals, Faculty Advisors, Industry Mentors
Career Connection
A high-quality and impactful dissertation is a powerful testament to your research and development capabilities, opening doors to cutting-edge R&D roles, prestigious PhD programs, and highly specialized AI positions globally and within India.
Network and Attend Conferences/Workshops- (Semester 6-10)
Leverage institutional events, the robust alumni network, and professional platforms like LinkedIn to connect with leading professionals and researchers in AI. Attend national and international AI conferences (e.g., CODS-COMAD, CIKM, local workshops) to stay updated on emerging trends, present your research work, and build a strong professional presence and network.
Tools & Resources
LinkedIn, Professional Conferences (e.g., CODS-COMAD, CIKM), Alumni Network
Career Connection
Networking is vital for discovering advanced career opportunities, fostering collaborations, and gaining invaluable insights into current industry challenges, paving the way for leadership and innovation roles in India''''s rapidly evolving AI ecosystem.
Program Structure and Curriculum
Eligibility:
- Admission through JEE Advanced, followed by JoSAA/CSAB counseling based on All India Rank. Specific institutional rules apply for dual degree specialization allocation for the School of Artificial Intelligence.
Duration: 10 semesters (5 years)
Credits: 240 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA10001 | Mathematics - I | Institute Core | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Multivariable Calculus, Vector Calculus |
| PH10001 | Physics - I | Institute Core | 4 | Classical Mechanics, Special Theory of Relativity, Electromagnetism, Optics, Quantum Mechanics Introduction |
| ME10001 | Engineering Drawing & Computer Graphics | Institute Core | 4 | Engineering Curves, Orthographic Projections, Sectional Views, Isometric Projections, Computer Graphics Basics |
| EV10001 | Environmental Science | Institute Core | 3 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Sustainable Development, Environmental Policies |
| CH10001 | Chemistry - I | Institute Core | 4 | Atomic Structure and Bonding, Thermodynamics, Chemical Kinetics, Electrochemistry, Spectroscopy Fundamentals |
| CS10001 | Programming & Data Structures | Institute Core | 4 | C Programming Basics, Arrays and Pointers, Functions and Recursion, Structures and Unions, Basic Data Structures (Lists, Stacks, Queues) |
| HS10001 | English for Communication | Institute Core | 2 | Grammar and Vocabulary, Written Communication, Oral Communication, Reading Comprehension, Report Writing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA10002 | Mathematics - II | Institute Core | 4 | Linear Algebra, Ordinary Differential Equations, Laplace Transforms, Fourier Series, Complex Analysis Introduction |
| PH10002 | Physics - II | Institute Core | 4 | Semiconductor Physics, Solid State Physics, Laser Physics, Fiber Optics, Quantum Mechanics Advanced |
| EC10001 | Basic Electronics | Institute Core | 4 | Diode Circuits, Transistor Biasing, Amplifiers, Operational Amplifiers, Digital Logic Gates |
| EE10001 | Basic Electrical Engineering | Institute Core | 4 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| CY19001 | Chemistry Laboratory | Institute Core | 2 | Volumetric Analysis, pH Metry, Conductometry, Spectrophotometry, Organic Synthesis Experiments |
| CE10001 | Engineering Mechanics | Institute Core | 4 | Statics of Particles, Rigid Bodies, Distributed Forces, Kinematics of Rigid Bodies, Kinetics of Rigid Bodies |
| CS19001 | Programming and Data Structures Lab | Institute Core | 2 | C Programming Exercises, Array and String Manipulations, Linked List Implementations, Stack and Queue Operations, Sorting and Searching Algorithms |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI20001 | Introduction to Artificial Intelligence | Department Core | 3 | AI Foundations, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics |
| AI20002 | Data Structures and Algorithms | Department Core | 3 | Advanced Data Structures (Trees, Graphs), Sorting and Hashing, Algorithm Design Paradigms, Complexity Analysis, Network Flow Algorithms |
| AI20003 | Discrete Structures | Department Core | 3 | Set Theory, Logic and Proofs, Combinatorics, Graph Theory, Algebraic Structures |
| AI20004 | Probability and Statistics | Department Core | 3 | Probability Theory, Random Variables, Statistical Inference, Hypothesis Testing, Regression Analysis |
| AI29001 | AI Lab - I (Programming) | Department Core (Lab) | 2 | Python for AI, Data Manipulation with Pandas, Numpy for Scientific Computing, Basic ML Libraries (Scikit-learn), Exploratory Data Analysis |
| AI29002 | Data Structures and Algorithms Lab | Department Core (Lab) | 2 | Tree Traversals, Graph Algorithms (BFS, DFS), Dynamic Programming Problems, Greedy Algorithms, Implementation of Hashing |
| HS20001 | Economics and Management | Institute Elective | 3 | Microeconomics Principles, Macroeconomics Principles, Financial Management, Marketing Management, Human Resource Management |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI20005 | Machine Learning | Department Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation and Selection, Ensemble Methods |
| AI20006 | Database Management Systems | Department Core | 3 | Relational Model, SQL Queries, Database Design, Transaction Management, NoSQL Databases Introduction |
| AI20007 | Design and Analysis of Algorithms | Department Core | 3 | Asymptotic Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, NP-Completeness |
| AI20008 | Operating Systems | Department Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| AI29003 | Machine Learning Lab | Department Core (Lab) | 2 | Linear Regression Implementation, Logistic Regression Implementation, Clustering Algorithms, Decision Tree Construction, Neural Network Basics with Keras/PyTorch |
| AI29004 | DBMS Lab | Department Core (Lab) | 2 | SQL Practice (DDL, DML), Database Normalization, Stored Procedures and Triggers, Query Optimization, NoSQL Database Operations |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI30001 | Deep Learning | Department Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers |
| AI30002 | Natural Language Processing | Department Core | 3 | Text Preprocessing, Language Models, Sequence Tagging, Machine Translation, Sentiment Analysis |
| AI30003 | Computer Vision | Department Core | 3 | Image Representation, Feature Extraction, Object Detection, Image Segmentation, Video Analysis |
| AI39001 | Deep Learning Lab | Department Core (Lab) | 2 | CNN Implementation for Image Classification, RNN Implementation for Sequence Prediction, Generative Model Training, Transfer Learning Techniques, Frameworks like TensorFlow/PyTorch |
| HS Elective | Humanities & Social Sciences Elective | Institute Elective | 3 | Ethics in AI, Cognitive Science, Sociology of Technology, Philosophy of Mind, Critical Thinking |
| DE-1 | Department Elective - I | Department Elective | 3 | Advanced topics in AI, Specialized algorithms, Emerging trends, Research methodologies, Industry applications |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI30004 | Reinforcement Learning | Department Core | 3 | Markov Decision Processes, Dynamic Programming in RL, Monte Carlo Methods, Temporal Difference Learning, Deep Reinforcement Learning |
| AI30005 | Big Data Analytics | Department Core | 3 | Hadoop Ecosystem, Spark Framework, Distributed File Systems, Big Data Storage, Real-time Data Processing |
| AI39002 | Reinforcement Learning Lab | Department Core (Lab) | 2 | OpenAI Gym Environments, Q-Learning Implementation, Policy Gradient Methods, Actor-Critic Algorithms, Simulation-based RL Experiments |
| DE-2 | Department Elective - II | Department Elective | 3 | Advanced AI models, Specific application domains, Current research problems, Algorithm optimizations, Ethical AI considerations |
| OE-1 | Open Elective - I | Open Elective | 3 | Interdisciplinary topics, Skill development, General engineering concepts, Entrepreneurship, Management principles |
| Summer Training/Project | Summer Training/Project | Project | 5 | Industry Internship, Research Project, Software Development, Data Analysis, Report Writing |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI40001 | AI Ethics and Governance | Department Core | 3 | Ethical Principles in AI, Bias and Fairness, Accountability and Transparency, Privacy and Security, AI Regulations and Policy |
| AI40002 | Human-Computer Interaction in AI | Department Core | 3 | User-Centered Design, Interaction Design Principles, Usability Evaluation, AI System Interfaces, Conversational AI Design |
| DE-3 | Department Elective - III | Department Elective | 3 | Specialized areas in AI, Cognitive computing, Robotics and AI, Edge AI, Quantum AI |
| OE-2 | Open Elective - II | Open Elective | 3 | Cross-disciplinary studies, Innovation and startups, Advanced computing concepts, Societal impact of technology, Foreign language |
| AI47001 | Project Part - I | Project | 5 | Problem Definition, Literature Review, System Design, Initial Implementation, Project Proposal and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DE-4 | Department Elective - IV | Department Elective | 3 | Advanced Machine Learning, Bayesian AI, Causal Inference, Explainable AI (XAI), Graph Neural Networks |
| OE-3 | Open Elective - III | Open Elective | 3 | Digital Humanities, Financial Technology, Bioinformatics, Operations Research, Public Policy |
| AI47002 | Project Part - II | Project | 8 | Detailed Implementation, Testing and Evaluation, Results Analysis, Technical Report Writing, Final Presentation and Defense |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI60001 | Advanced Topics in AI | Department Core (M.Tech) | 3 | Advanced Algorithmic Foundations, Complex AI Architectures, AI System Integration, Distributed AI, Research Frontiers |
| DE-5 | Department Elective - V | Department Elective (M.Tech) | 3 | Specific AI sub-fields, Research methodology, Case studies, Emerging technologies, Advanced algorithms |
| DE-6 | Department Elective - VI | Department Elective (M.Tech) | 3 | Deep dive into specialized areas, Applied AI projects, System optimization, Novel architectures, Performance evaluation |
| AI68001 | Seminar / Industrial Training | Seminar/Training (M.Tech) | 3 | Literature Review, Presentation Skills, Industry Best Practices, Problem Solving in Industry, Technical Report Preparation |
| AI67001 | Project / Dissertation Part - I | Project (M.Tech) | 8 | Research Problem Identification, Methodology Development, Experimental Setup Design, Initial Results, Thesis Proposal |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DE-7 | Department Elective - VII | Department Elective (M.Tech) | 3 | Highly specialized AI topics, Frontier research, Interdisciplinary AI applications, Advanced data science, Security in AI |
| AI67002 | Project / Dissertation Part - II | Project (M.Tech) | 14 | Advanced System Implementation, Extensive Experimentation, Detailed Data Analysis, Thesis Writing, Final Dissertation Defense |
