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M-TECH in Artificial Intelligence at Indian Institute of Science
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence at Indian Institute of Science Bengaluru?
This Artificial Intelligence (AI) program at the Indian Institute of Science (IISc), Bengaluru, focuses on providing a strong foundation in core AI principles, machine learning, deep learning, and reinforcement learning. It addresses the growing demand for skilled AI professionals in India''''s rapidly expanding technology sector, preparing students for cutting-edge research and development roles. The program distinguishes itself through its rigorous, research-oriented curriculum and interdisciplinary approach.
Who Should Apply?
This program is ideal for engineering graduates, particularly from computer science, electronics, electrical, or related disciplines, as well as science post-graduates with strong mathematical and computational backgrounds. It caters to fresh graduates aspiring to enter the AI domain, working professionals seeking to upskill or transition into AI-centric roles, and individuals keen on pursuing advanced research in artificial intelligence. A valid GATE score is a prerequisite for admission.
Why Choose This Course?
Graduates of this program can expect to secure high-impact roles as AI/ML engineers, Data Scientists, Research Scientists, or AI Architects in top Indian and multinational companies. Starting salaries for entry-level positions typically range from INR 10-25 lakhs per annum, with significant growth potential as experience increases. The strong theoretical and practical grounding facilitates career progression towards leadership and specialist roles in India''''s booming AI industry, including startups and R&D centers.
Student Success Practices
Foundation Stage
Master Core AI and ML Fundamentals- (Semester 1-2)
Dedicate significant time to thoroughly understand the mathematical and algorithmic foundations of AI, Machine Learning, and Deep Learning. Actively participate in lectures, clarify doubts with professors, and solve all assigned problems. Form study groups with peers to discuss complex concepts and reinforce learning.
Tools & Resources
Textbooks on ML/DL (e.g., Bishop, Goodfellow), NPTEL courses, IISc library resources, Peer study groups
Career Connection
A strong grasp of fundamentals is crucial for passing technical interviews, building robust AI systems, and adapting to new technologies throughout your career in AI.
Build a Strong Programming Portfolio- (Semester 1-2)
Beyond coursework, actively engage in coding challenges and personal projects involving Python, TensorFlow/PyTorch. Focus on implementing algorithms from scratch and participating in Kaggle competitions or hackathons. Contribute to open-source AI projects to gain practical experience and showcase skills.
Tools & Resources
LeetCode, HackerRank, Kaggle, GitHub, Python with NumPy, Pandas, TensorFlow/PyTorch
Career Connection
A solid coding portfolio demonstrates practical problem-solving abilities and is a key differentiator in placement drives, especially for AI/ML engineering roles.
Network with Faculty and Senior Researchers- (Semester 1-2)
Regularly attend department seminars, research talks, and workshops. Engage with faculty members to understand their research areas and explore potential M.Tech project topics. Seek mentorship from senior PhD students for guidance on academic and research pathways.
Tools & Resources
Department seminar schedules, IISc research group pages, LinkedIn
Career Connection
Early networking can lead to valuable research opportunities, project guidance, and strong recommendation letters, critical for both academic and industry careers.
Intermediate Stage
Deep Dive into Specialization Electives- (Semester 2-3)
Strategically choose elective courses that align with your specific interests within AI, such as NLP, Computer Vision, Reinforcement Learning, or Generative AI. Aim to take advanced courses and dedicate extra time to applied projects in these chosen areas to build expertise beyond the curriculum.
Tools & Resources
IISc Elective Course Catalog, Research papers in your chosen sub-field, Specialized online courses (Coursera, edX)
Career Connection
Specialized knowledge makes you a strong candidate for specific roles and niche areas in the AI industry, enhancing your value to potential employers.
Pursue Industry Internships- (Between Semester 2 and 3, or during Semester 3)
Actively seek and complete at least one summer or semester-long internship at a reputable AI company, startup, or research lab. Focus on gaining hands-on experience with real-world datasets, deploying AI models, and working in a professional team environment. Utilize IISc''''s strong industry connections for opportunities.
Tools & Resources
IISc Placement Cell, LinkedIn, Naukri.com, Company career portals
Career Connection
Internships are invaluable for gaining industry exposure, building professional networks, and often lead to pre-placement offers (PPOs), directly impacting your job prospects.
Participate in AI Research Projects- (Semester 3)
Engage in research projects, either as part of your M.Tech thesis or as independent study under a faculty member. Focus on identifying novel problems, designing innovative solutions, and documenting your findings. Aim to publish your work in conferences or journals.
Tools & Resources
IISc research labs, Academic conferences (NeurIPS, ICML, AAAI), Preprint servers (arXiv)
Career Connection
Research experience, especially publications, significantly boosts your profile for R&D roles, PhD admissions, and demonstrates your ability to contribute to the field.
Advanced Stage
Develop a Robust M.Tech Thesis/Project- (Semester 3-4)
Dedicate extensive effort to your M.Tech project, treating it as a significant research contribution. Aim for a novel problem, develop a sophisticated solution, and rigorously evaluate your results. Present your findings effectively in both written thesis and oral defense, demonstrating deep understanding and critical thinking.
Tools & Resources
IISc Thesis Guidelines, EndNote/Zotero for referencing, LaTeX for document preparation, Faculty advisors
Career Connection
A strong M.Tech thesis is your capstone achievement, showcasing your ability to conduct independent research, solve complex problems, and deliver impactful results, crucial for R&D roles and further studies.
Prepare Rigorously for Placements and Interviews- (Semester 4)
Start placement preparation early, focusing on advanced data structures, algorithms, system design, and AI/ML specific concepts. Practice mock interviews with peers and alumni, and refine your resume and cover letter to highlight AI-specific skills and project experiences. Attend career fairs and company presentations.
Tools & Resources
GeeksforGeeks, Educative.io, InterviewBit, IISc Placement Cell workshops, Alumni network
Career Connection
Comprehensive preparation is key to securing top placements in leading tech and research companies, ensuring you articulate your technical skills and project contributions effectively.
Cultivate a Lifelong Learning Mindset- (Beyond Semester 4)
The AI field evolves rapidly, so commit to continuous learning. Follow leading AI researchers, subscribe to relevant journals and conferences, and explore new frameworks and techniques post-graduation. Engage in online communities and contribute to the open-source ecosystem.
Tools & Resources
arXiv.org, Twitter (AI researchers), Medium/Towards Data Science, GitHub, AI conference proceedings
Career Connection
Adaptability and continuous learning are critical for long-term career growth in AI, enabling you to stay relevant and lead innovation in the ever-changing technological landscape.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. or equivalent degree in any branch of engineering, or Master''''s degree in any branch of science / Master''''s degree in computer applications / B.S. (4-year) or B.Tech (4-year) in Physics / Computer Science / Mathematics / Statistics with a valid GATE score in relevant disciplines (CS, EC, EE, MA, ST, PH). Minimum of 50% / 5.5 CGPA in the qualifying examination. Eligibility requirements are subject to change as per IISc admission guidelines.
Duration: 2 years (4 semesters)
Credits: 55 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 241 | Foundations of Artificial Intelligence | Core | 3 | Problem Solving by Search, Adversarial Search (Game Playing), Constraint Satisfaction Problems, Knowledge Representation and Reasoning, Uncertainty and Probabilistic Reasoning |
| E0 242 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, Dimensionality Reduction), Model Evaluation and Selection, Bias-Variance Tradeoff, Support Vector Machines, Ensemble Methods |
| E0 243 | Deep Learning | Core | 3 | Neural Networks Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention Mechanisms and Transformers, Generative Models (GANs, VAEs) |
| ELECTIVES GROUP A POOL | Electives from Group A (Student Choice) | Elective (Pool) | Variable (3 per course) | Students select a minimum of three courses from this pool across semesters 1, 2, and 3 based on their interests and specialization. This entry represents the pool for structural purposes. |
| E0 246 | Advanced Machine Learning | Elective (Group A Pool) | 3 | Advanced supervised and unsupervised learning, Bayesian methods, Kernel methods, Dimensionality reduction, Graphical models |
| E0 247 | Machine Learning for Signal Processing | Elective (Group A Pool) | 3 | ML applications to audio, image, time-series data, Feature extraction techniques, Spectral analysis methods, Deep learning for signals, Pattern recognition in signals |
| E0 248 | Machine Learning for Computer Vision | Elective (Group A Pool) | 3 | Deep learning for image classification, Object detection and segmentation, Generative models for vision, Video analysis, Image understanding |
| E0 249 | Natural Language Processing | Elective (Group A Pool) | 3 | Text representation and embeddings, Language models (statistical and neural), Syntactic parsing, semantic analysis, Machine translation, information retrieval, Question answering systems |
| E0 250 | AI for Healthcare | Elective (Group A Pool) | 3 | Medical image analysis, Diagnostic prediction models, Drug discovery applications, Personalized medicine systems, Ethical considerations in healthcare AI |
| E0 251 | Game Theory and Multi-Agent Systems | Elective (Group A Pool) | 3 | Strategic and extensive form games, Nash equilibrium, mechanism design, Repeated games, cooperative games, Multi-agent planning and learning, Collective decision making |
| E0 252 | Robotics and Autonomous Systems | Elective (Group A Pool) | 3 | Robot kinematics and dynamics, Robot control strategies, Motion planning and navigation, Localization and mapping (SLAM), Human-robot interaction |
| E0 253 | AI Ethics and Society | Elective (Group A Pool) | 3 | Fairness, accountability, transparency in AI, Bias detection and mitigation, Ethical guidelines for AI development, Societal impact and policy frameworks, Privacy and data governance |
| E0 254 | Advanced Deep Learning | Elective (Group A Pool) | 3 | Advanced CNN and RNN architectures, Graph neural networks (GNNs), Self-supervised and contrastive learning, Meta-learning techniques, Neural architecture search |
| E0 255 | Bayesian Machine Learning | Elective (Group A Pool) | 3 | Bayesian inference fundamentals, Gaussian processes, Variational inference, Markov Chain Monte Carlo (MCMC) methods, Bayesian neural networks |
| E0 256 | Causal Inference for Machine Learning | Elective (Group A Pool) | 3 | Potential outcomes framework, Causal graphs and DAGs, Instrumental variables, Confounding adjustment methods, Mediation analysis |
| E0 257 | Federated Learning | Elective (Group A Pool) | 3 | Distributed machine learning principles, Privacy-preserving techniques, Secure aggregation methods, Communication efficiency in FL, Applications in mobile and edge AI |
| E0 258 | Generative AI | Elective (Group A Pool) | 3 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models for content generation, Autoregressive models, Prompt engineering techniques |
| E0 259 | Large Language Models | Elective (Group A Pool) | 3 | Transformer architecture, Pre-training strategies, Fine-tuning and adaptation techniques, Prompt design and engineering, Ethical considerations and applications |
| E0 260 | Explainable AI | Elective (Group A Pool) | 3 | Interpretability vs. explainability, Model-agnostic methods (LIME, SHAP), Model-specific explanation techniques, Ethical aspects of XAI, Human-in-the-loop AI |
| E0 261 | Geometric Deep Learning | Elective (Group A Pool) | 3 | Graph Neural Networks (GNNs), Processing of non-Euclidean data, Applications in social networks, Molecular data analysis, 3D vision and geometry processing |
| E0 262 | Learning for Combinatorial Optimization | Elective (Group A Pool) | 3 | Graph Neural Networks for optimization, Reinforcement learning for combinatorial problems, Metaheuristics and problem-solving strategies, Neural combinatorial optimization, Approximate algorithms |
| E0 263 | Sequential Decision Making | Elective (Group A Pool) | 3 | Dynamic programming principles, Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), Optimal control theory, Planning under uncertainty |
| E0 264 | Spectral Graph Theory for Machine Learning | Elective (Group A Pool) | 3 | Graph Laplacian and its properties, Eigenvalues and eigenvectors of graphs, Graph clustering algorithms, Dimensionality reduction on graphs, Network analysis applications |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 244 | Reinforcement Learning | Core | 3 | Markov Decision Processes (MDPs), Dynamic Programming Methods, Monte Carlo and Temporal Difference Learning (Q-learning, SARSA), Policy Gradient Algorithms, Deep Reinforcement Learning |
| E0 245 | Probabilistic Graphical Models | Core | 3 | Bayesian Networks, Markov Random Fields, Exact Inference (Variable Elimination, Sum-Product), Approximate Inference (Sampling, Variational), Learning Parameters and Structure |
| ELECTIVES GROUP B POOL | Electives from Group B (Student Choice) | Elective (Pool) | Variable (3 per course) | Students select a minimum of two courses from this pool across semesters 2 and 3 based on their interests and specialization. This entry represents the pool for structural purposes. |
| E0 281 | Optimization for Machine Learning | Elective (Group B Pool) | 3 | Convex optimization fundamentals, Unconstrained optimization methods, Constrained optimization and KKT conditions, Gradient-based algorithms (SGD, Adam), Stochastic optimization techniques |
| E0 282 | Information Theory and Coding | Elective (Group B Pool) | 3 | Entropy and mutual information, Source coding (Huffman, Lempel-Ziv), Channel capacity (Shannon''''s theorems), Error-correcting codes, Rate distortion theory |
| E0 283 | Digital Signal Processing | Elective (Group B Pool) | 3 | Discrete-time signals and systems, Z-transform and DFT/FFT, Digital filter design (FIR, IIR), Multirate signal processing, Adaptive filtering concepts |
| E0 284 | Computer Vision | Elective (Group B Pool) | 3 | Image formation and perception, Feature detection and description, Image segmentation and grouping, Object recognition techniques, 3D vision and motion analysis |
| E0 285 | Pattern Recognition | Elective (Group B Pool) | 3 | Statistical pattern recognition, Neural networks for pattern classification, Support Vector Machines (SVMs), Clustering algorithms, Feature selection and extraction |
| E0 286 | Convex Optimization | Elective (Group B Pool) | 3 | Convex sets and functions, Convex optimization problems formulation, Duality theory and KKT conditions, Numerical algorithms (interior-point, cutting-plane), Applications in engineering and finance |
| E0 287 | Cryptography and Blockchain | Elective (Group B Pool) | 3 | Symmetric and asymmetric cryptography, Hash functions and digital signatures, Blockchain architecture and distributed ledgers, Consensus mechanisms (PoW, PoS), Cryptocurrency concepts |
| E0 288 | Quantum Computing | Elective (Group B Pool) | 3 | Quantum mechanics fundamentals (qubits, superposition), Quantum gates and circuits, Quantum algorithms (Shor''''s, Grover''''s), Quantum error correction, Quantum computing platforms |
| E0 289 | Advanced Algorithms | Elective (Group B Pool) | 3 | Graph algorithms (network flow, matching), Computational geometry, String algorithms, Approximation algorithms, Randomized algorithms and complexity theory |
| E0 290 | Human-Computer Interaction | Elective (Group B Pool) | 3 | Usability and user-centered design principles, Evaluation techniques (heuristic, user testing), Interaction design paradigms, Cognitive aspects of HCI, Current trends (VR/AR, tangible UI) |
| E0 291 | Reinforcement Learning for Control | Elective (Group B Pool) | 3 | Optimal control theory, Adaptive control systems, Model predictive control (MPC), Application of RL in robotic control, Stochastic control problems |
| E0 292 | Digital Image Processing | Elective (Group B Pool) | 3 | Image enhancement and restoration, Image compression techniques, Morphological image processing, Color image processing, Wavelet transforms for images |
| E0 293 | Advanced Digital Signal Processing | Elective (Group B Pool) | 3 | Multirate signal processing systems, Adaptive filters and applications, Spectral estimation techniques, Wavelet theory and applications, Array signal processing |
| E0 294 | Introduction to Robotics | Elective (Group B Pool) | 3 | Robot manipulators kinematics, Mobile robot navigation, Sensors and actuators for robots, Robot control architectures, Robot programming paradigms |
| E0 295 | Advanced Computer Networks | Elective (Group B Pool) | 3 | Network architectures and protocols, Routing algorithms and congestion control, Wireless and mobile networks, Software-defined networking (SDN), Network security principles |
| E0 296 | Distributed Computing Systems | Elective (Group B Pool) | 3 | Distributed system models (client-server, P2P), Consistency and replication mechanisms, Fault tolerance and recovery, Distributed transactions, Cloud computing architectures |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| E0 200 | Technical Seminar | Seminar | 1 | Literature survey on a specialized topic, Research paper presentation skills, Technical communication, Critical analysis of research, Independent study and presentation |
| MT AI PROJ 3 | M.Tech. Project Phase I | Project | Part of 24 Total | Problem identification and literature review, Methodology development, Initial implementation and experimentation, Mid-term progress reporting, Setting milestones for phase II |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MT AI PROJ 4 | M.Tech. Project Phase II | Project | Part of 24 Total | Advanced implementation and system development, Extensive experimentation and result analysis, Thesis writing and documentation, Final defense and presentation, Contribution to research/industry |
