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M-TECH in Artificial Intelligence And Robotics at Indian Institute of Technology Mandi
Mandi, Himachal Pradesh
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About the Specialization
What is Artificial Intelligence and Robotics at Indian Institute of Technology Mandi Mandi?
This Artificial Intelligence and Robotics program at IIT Mandi focuses on cutting-edge research and development in intelligent systems and autonomous robots. It integrates core AI principles with advanced robotics, addressing critical needs in India''''s growing automation and smart technology sectors. The program uniquely blends theoretical knowledge with hands-on experience, preparing students for innovative roles.
Who Should Apply?
This program is ideal for engineering graduates with a background in Computer Science, IT, Electronics, or Electrical Engineering, eager to specialize in AI and Robotics. It also welcomes working professionals looking to upskill in areas like deep learning, computer vision, and robotic control, or career changers aiming to transition into the high-demand AI and automation industry in India.
Why Choose This Course?
Graduates of this program can expect to secure roles as AI Engineers, Robotics Developers, Machine Learning Scientists, or Automation Architects in leading Indian and global firms. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning significantly more. The program prepares students for leadership in R&D, product development, and academic research.
Student Success Practices
Foundation Stage
Master Core AI/ML and Robotics Concepts- (Semester 1-2)
Dedicate time to deeply understand foundational subjects like Deep Learning, Advanced Algorithms, and Robotics. Actively participate in lab sessions for Machine Learning and AI & Robotics to build strong practical skills. Form study groups with peers to discuss complex topics and solve problems collaboratively, enhancing comprehension and retention.
Tools & Resources
Python, TensorFlow, PyTorch, ROS, Online courses (Coursera, NPTEL) for theoretical reinforcement
Career Connection
A strong foundation is critical for advanced courses and directly impacts performance in technical interviews for core AI/ML/Robotics roles.
Engage in Self-Paced Project Work- (Semester 1-2)
Beyond coursework, undertake small personal projects using publicly available datasets and robotic simulation platforms. This practical application solidifies theoretical knowledge and helps identify areas of personal interest. Document your projects thoroughly on platforms like GitHub to showcase your capabilities.
Tools & Resources
Kaggle, GitHub, Google Colab, Gazebo simulator, OpenAI Gym
Career Connection
Demonstrable project experience is highly valued by recruiters and significantly strengthens your resume for internships and placements.
Network and Participate in Technical Events- (Semester 1-2)
Attend department seminars, workshops, and guest lectures by industry experts. Join relevant student clubs focused on AI, Robotics, or Data Science. Participating in internal hackathons or coding competitions fosters problem-solving skills and exposes you to new ideas and potential collaborators.
Tools & Resources
LinkedIn, Institute''''s technical clubs, Hackathon platforms
Career Connection
Building an early professional network can open doors to mentorship, internships, and future job opportunities in the Indian tech ecosystem.
Intermediate Stage
Strategically Choose Electives for Specialization- (Semester 2-3)
Research the various elective offerings carefully, aligning your choices with your career aspirations (e.g., NLP, Computer Vision, advanced robotics control). Consult with faculty advisors to understand the practical applications and industry relevance of different specialized courses.
Tools & Resources
Course catalogue, Faculty research profiles, Industry trend reports
Career Connection
Focused specialization through electives directly prepares you for niche roles and advanced research opportunities in your chosen field.
Pursue Research and Publications- (Semester 3-4)
Actively seek opportunities to work with faculty on research projects, even if small in scope initially. Aim to contribute to research papers, attend conferences, and consider publishing in reputed journals or workshops. This builds critical thinking and research methodology skills.
Tools & Resources
Institute''''s research labs, arXiv, Google Scholar, Conference proceedings
Career Connection
Publications and research experience are invaluable for academic careers, R&D roles, and admission to Ph.D. programs, especially in India''''s research institutions.
Engage in Internships and Industry Projects- (Semester 3)
Actively apply for internships during summer breaks or dedicated project semesters at AI/Robotics companies, startups, or research labs. Gaining real-world experience is crucial. Focus on applying learned concepts to solve actual industry problems, documenting your contributions thoroughly.
Tools & Resources
Institute''''s career services, Naukri.com, Internshala, LinkedIn Jobs
Career Connection
Internships often lead to pre-placement offers (PPOs) and provide practical exposure, making you highly employable in the competitive Indian job market.
Advanced Stage
Excel in M.Tech Project for Impact- (Semester 3-4)
Treat your M.Tech project as a capstone experience. Choose a challenging problem, conduct thorough literature reviews, and aim for an innovative solution. Prioritize clear documentation, strong experimental validation, and high-quality thesis writing, aiming for potential patents or publications.
Tools & Resources
Research papers, Patent databases, Version control (Git), Academic writing tools
Career Connection
A high-impact project showcases your independent research and problem-solving abilities, which are highly valued for both industry R&D and academic positions.
Develop Advanced Soft Skills and Communication- (Semester 4)
Participate in workshops on presentation skills, technical writing, and professional communication. Engage in peer reviews of research papers and presentations. Practice articulating complex technical concepts clearly and concisely, preparing for interviews and future leadership roles.
Tools & Resources
Toastmasters International (if available), Institute''''s communication center, Mock interview sessions
Career Connection
Strong communication skills are essential for career progression, enabling you to present ideas effectively to technical and non-technical stakeholders, crucial for leadership roles.
Strategize for Placements or Further Studies- (Semester 4)
Begin placement preparation early by revising core concepts, solving coding problems, and practicing case studies relevant to AI/Robotics roles. For higher studies, focus on GATE/GRE/TOEFL preparation, identify potential Ph.D. advisors, and prepare compelling statements of purpose and research proposals.
Tools & Resources
GeeksforGeeks, LeetCode, Previous year placement papers, University websites for Ph.D. programs
Career Connection
Proactive and targeted preparation ensures you are well-positioned for top placements in Indian and global companies or for admission to prestigious Ph.D. programs worldwide.
Program Structure and Curriculum
Eligibility:
- B.Tech/B.E. in CS/IT/EE/ECE or equivalent with valid GATE score (e.g., CS, EC, EE). Or M.Sc. in CS/IT/Mathematics/Statistics/Physics/Electronics or MCA with valid GATE score (e.g., CS, MA, PH, EC, EE). Minimum marks/CGPA as per institute norms (typically 60% or 6.0 CGPA).
Duration: 2 years (4 semesters)
Credits: 54 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS601 | Deep Learning | Core | 3 | Introduction to Deep Learning, Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders and GANs, Deep Reinforcement Learning |
| CS602 | Advanced Algorithms | Core | 3 | Amortized Analysis, Graph Algorithms, Flow Networks, Linear Programming, NP-completeness, Approximation Algorithms |
| CS603 | Robotics | Core | 3 | Robot Kinematics, Dynamics, Trajectory Generation, Motion Planning, Control Architectures, Robot Learning |
| CS604 | Machine Learning Lab | Lab | 2 | Python for ML, Supervised Learning, Unsupervised Learning, Model Evaluation, Deep Learning Frameworks, AI Libraries |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS605 | Computer Vision | Core | 3 | Image Formation, Feature Detection, Image Segmentation, Multiple View Geometry, Object Recognition, Deep Learning for Vision |
| CS606 | Natural Language Processing | Core | 3 | Language Models, Part-of-Speech Tagging, Syntactic Parsing, Semantic Analysis, Machine Translation, Deep Learning for NLP |
| CS607 | AI and Robotics Lab | Lab | 2 | Robot Simulation, Robot Control, Vision for Robotics, NLP Applications, Reinforcement Learning Projects, Integration of AI and Robotics |
| CS699 | Seminar | Core | 2 | Research Methodology, Technical Writing, Presentation Skills, Literature Review, Current Research Trends, Project Proposal Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS799 | M.Tech Project Part-I | Project | 6 | Problem Identification, Literature Survey, Methodology Design, Initial Implementation, Preliminary Results, Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS899 | M.Tech Project Part-II | Project | 16 | Advanced Implementation, Experimental Validation, Performance Analysis, Thesis Writing, Defense Preparation, Research Publication |
Semester electives
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS511 | Pattern Recognition | Elective | 3 | Bayesian decision theory, Maximum likelihood estimation, Non-parametric methods, Linear discriminant functions, Unsupervised learning (clustering), Feature extraction and selection |
| CS512 | Cryptography | Elective | 3 | Symmetric key ciphers, Asymmetric key ciphers, Hash functions, Digital signatures, Key management, Blockchain applications |
| CS513 | Advanced Data Structures | Elective | 3 | Amortized analysis, Splay trees, Fibonacci heaps, Disjoint set data structures, Segment trees, Fenwick trees |
| CS514 | Randomized Algorithms | Elective | 3 | Las Vegas algorithms, Monte Carlo algorithms, Probabilistic analysis, Hashing, Random walks, Graph algorithms |
| CS515 | Approximation Algorithms | Elective | 3 | NP-hard problems, Absolute approximations, Relative approximations, Vertex Cover, Set Cover, Traveling Salesperson Problem |
| CS516 | Quantum Computing | Elective | 3 | Quantum mechanics basics, Qubits and quantum gates, Quantum algorithms (Shor''''s, Grover''''s), Quantum error correction, Quantum hardware, Quantum simulation |
| CS517 | High Performance Computing | Elective | 3 | Parallel computing architectures, Shared memory programming (OpenMP), Distributed memory programming (MPI), GPU programming (CUDA), Performance analysis, Cloud HPC |
| CS518 | Advanced Computer Networks | Elective | 3 | Network protocols, Software Defined Networking (SDN), Network Function Virtualization (NFV), Wireless networks, Mobile ad-hoc networks, Network security |
| CS519 | Blockchain Technology | Elective | 3 | Cryptographic primitives, Distributed consensus, Bitcoin and Ethereum, Smart contracts, Enterprise blockchains, DApps and DeFi |
| CS520 | Software Defined Networking | Elective | 3 | SDN architecture, OpenFlow protocol, Controllers (POX, ONOS), Network virtualization, Traffic engineering, Security in SDN |
| CS521 | Distributed Systems | Elective | 3 | Consistency models, Consensus protocols (Paxos, Raft), Distributed transactions, Fault tolerance, Distributed file systems, Message passing |
| CS522 | Cloud Computing | Elective | 3 | Cloud service models (IaaS, PaaS, SaaS), Virtualization, Containerization, Resource management, Cloud security, Serverless computing |
| CS523 | Big Data Analytics | Elective | 3 | Hadoop ecosystem (HDFS, MapReduce), Spark, NoSQL databases, Data streaming, Data warehousing, Data visualization |
| CS524 | Internet of Things | Elective | 3 | IoT architecture, Sensing and actuation, IoT communication protocols, Data processing and analytics, IoT security, Edge computing |
| CS525 | Cyber Physical Systems | Elective | 3 | Introduction to CPS, Modeling and design of CPS, Control systems, Real-time systems, Security and privacy in CPS, Applications (smart grid, autonomous vehicles) |
| CS526 | Trustworthy AI | Elective | 3 | AI ethics principles, Fairness, Accountability, Transparency (FAT), Bias detection and mitigation, Privacy-preserving AI, Robustness against adversarial attacks, Ethical considerations |
| CS527 | Ethical AI | Elective | 3 | Moral philosophy for AI, AI societal impact, Algorithmic bias, Data privacy concerns, AI governance, Responsible AI development |
| CS528 | AI for Social Good | Elective | 3 | Applications in healthcare, Education, Environment, Disaster response, Poverty alleviation, Ethical considerations in deployment |
| CS529 | Reinforcement Learning | Elective | 3 | Markov Decision Processes, Dynamic programming, Monte Carlo methods, Temporal difference learning, Policy gradient, Deep Q-Networks |
| CS530 | Generative Models | Elective | 3 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive models, Diffusion models, Latent space learning, Image and text generation |
| CS531 | Explainable AI | Elective | 3 | Interpretability vs Explainability, Local and global explanations, SHAP, LIME, Model distillation, Counterfactual explanations, Ethical implications |
| CS532 | Federated Learning | Elective | 3 | Privacy-preserving ML, Collaborative training, Aggregation algorithms, Communication efficiency, Security challenges, Applications (healthcare, mobile devices) |
| CS533 | Bio-inspired AI | Elective | 3 | Swarm intelligence, Evolutionary algorithms, Neural networks, Ant colony optimization, Particle swarm optimization, Genetic algorithms |
| CS534 | Cognitive Robotics | Elective | 3 | Embodied AI, Action perception loops, Learning from demonstration, Human-robot collaboration, Imitation learning, Explainable robot behavior |
| CS535 | Human-Robot Interaction | Elective | 3 | HRI paradigms, Social robotics, Trust and transparency, Non-verbal communication, Robot ethics, User studies in HRI |
| CS536 | Robot Operating System (ROS) | Elective | 3 | ROS architecture, Nodes, Topics, Services, Messages, ROS packages, Simulation with Gazebo, Robot hardware integration, Robot Programming with ROS |
| CS537 | Mobile Robotics | Elective | 3 | Robot locomotion, Kinematics and dynamics, Sensing and perception, Localization and mapping (SLAM), Path planning, Navigation |
| CS538 | Swarm Robotics | Elective | 3 | Collective behavior, Decentralized control, Emergent intelligence, Flocking algorithms, Self-organization, Applications (search and rescue) |
| CS539 | Advanced Control Systems for Robotics | Elective | 3 | PID control, Optimal control, Adaptive control, Robust control, Model predictive control, Force control |
| CS540 | Computer Graphics | Elective | 3 | Graphics pipeline, Transformations, Lighting and shading, Texturing, Rendering techniques, Ray tracing |
| CS541 | Virtual and Augmented Reality | Elective | 3 | VR/AR hardware, Display technologies, Tracking and sensing, 3D interaction, Haptic feedback, Applications |
| CS542 | Medical Image Analysis | Elective | 3 | Image acquisition, Preprocessing, Segmentation, Registration, Feature extraction, Machine learning for diagnosis |
| CS543 | Speech Processing | Elective | 3 | Speech production, Acoustic phonetics, Speech recognition, Speech synthesis, Speaker recognition, Emotion detection from speech |
| CS544 | Information Retrieval | Elective | 3 | Boolean models, Vector space models, Probabilistic models, Evaluation metrics, Web search, Recommender systems |
| CS545 | Recommender Systems | Elective | 3 | Collaborative filtering, Content-based filtering, Hybrid approaches, Matrix factorization, Deep learning for recommendations, Evaluation metrics |
| CS546 | Data Mining | Elective | 3 | Association rule mining, Classification, Clustering, Anomaly detection, Frequent pattern mining, Data preprocessing |
| CS547 | Machine Learning for Cybersecurity | Elective | 3 | Anomaly detection, Malware analysis, Intrusion detection, Spam filtering, Security event correlation, Adversarial machine learning |
| CS548 | Financial Technology (FinTech) | Elective | 3 | Blockchain in finance, Algorithmic trading, Robo-advisors, Peer-to-peer lending, Regulatory technology (RegTech), AI in banking |
| CS549 | Quantum Machine Learning | Elective | 3 | Quantum computation basics, Quantum algorithms for ML, Quantum neural networks, Quantum support vector machines, Quantum generative models, Challenges and prospects |
| CS550 | Advanced Optimization Techniques | Elective | 3 | Convex optimization, Non-linear optimization, Gradient descent methods, Stochastic optimization, Metaheuristics, Optimization for machine learning |
| CS551 | Game Theory | Elective | 3 | Nash equilibrium, Extensive form games, Cooperative games, Mechanism design, Repeated games, Applications in AI and economics |
| CS552 | Multi-agent Systems | Elective | 3 | Agent architectures, Communication and coordination, Cooperation and competition, Distributed AI, Game theory in MAS, Applications |
| CS553 | Computational Neuroscience | Elective | 3 | Neural coding, Synaptic plasticity, Neural networks, Brain models, Computational perception, Cognitive modeling |
| CS554 | Brain-Computer Interface | Elective | 3 | BCI paradigms, Signal acquisition (EEG, ECoG), Signal processing, Feature extraction, Classification, Applications (prosthetics, communication) |
| CS555 | Computational Linguistics | Elective | 3 | Formal grammars, Parsing techniques, Semantic networks, Lexical semantics, Dialogue systems, Computational morphology |
| CS556 | Digital Image Processing | Elective | 3 | Image enhancement, Image restoration, Image transforms, Morphological operations, Image compression, Color image processing |
| CS557 | Parallel and Distributed Algorithms | Elective | 3 | Concurrency control, Distributed consensus, Graph algorithms, Matrix operations, Sorting and searching, Performance analysis |
| CS558 | Network Science | Elective | 3 | Graph theory, Centrality measures, Community detection, Network robustness, Spreading phenomena, Complex networks |
| CS559 | Formal Methods for AI | Elective | 3 | Logic and knowledge representation, Verification of AI systems, Theorem proving, Model checking, Safe AI, Explainable AI with formal logic |
| CS560 | Semantic Web | Elective | 3 | RDF, RDFS, OWL, SPARQL, Ontologies, Linked Data, Knowledge graphs, Semantic search |
