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M-TECH in Artificial Intelligence at Indian Institute of Technology Roorkee
Haridwar, Uttarakhand
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About the Specialization
What is Artificial Intelligence at Indian Institute of Technology Roorkee Haridwar?
This Artificial Intelligence program at IIT Roorkee focuses on equipping students with advanced knowledge and practical skills in AI. It addresses the growing demand for skilled professionals in India''''s booming AI and machine learning sector, providing a robust foundation in theoretical concepts and hands-on applications.
Who Should Apply?
This program is ideal for engineering graduates with a background in Computer Science, IT, or related fields, and a valid GATE score. It caters to fresh graduates seeking entry into advanced AI roles and working professionals looking to upskill or transition into the rapidly evolving Indian AI industry.
Why Choose This Course?
Graduates of this program can expect to pursue high-demand careers as AI/ML Engineers, Data Scientists, Research Scientists, or AI Consultants in India. With strong industry demand, entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning significantly more. The program aligns with industry certifications in deep learning and cloud AI.
Student Success Practices
Foundation Stage
Master Core AI Mathematics and Algorithms- (Semester 1-2)
Focus rigorously on mathematical foundations (linear algebra, probability, optimization) and algorithm design. Utilize online platforms for problem-solving and implement data structures and algorithms from scratch to solidify understanding.
Tools & Resources
NPTEL courses on Linear Algebra/Probability, GeeksforGeeks, LeetCode, MIT OpenCourseware
Career Connection
A strong foundation is crucial for excelling in technical interviews for AI/ML roles and building complex models efficiently.
Build a Foundational Project Portfolio- (Semester 1-2)
Apply concepts learned in Machine Learning and Deep Learning to develop small, independent projects. Start with classic datasets and gradually move to more complex real-world problems. Document code and methodology on GitHub.
Tools & Resources
Kaggle for datasets and competitions, Google Colab/Jupyter Notebooks, GitHub for version control
Career Connection
Practical projects demonstrate application skills to potential employers, enhancing internship and placement prospects.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form study groups to discuss complex topics, solve problems collaboratively, and prepare for exams. Teach concepts to peers to deepen your own understanding and build a supportive academic network.
Tools & Resources
University library study rooms, WhatsApp/Discord groups
Career Connection
Improves communication skills, fosters teamwork, and provides alternative perspectives, all valuable for future professional collaborations.
Intermediate Stage
Specialize through Electives and Advanced Projects- (Semester 3)
Strategically choose electives that align with your career interests (e.g., NLP, Computer Vision, Reinforcement Learning). Work on advanced projects or research papers in these specialized areas, potentially collaborating with faculty.
Tools & Resources
ArXiv for research papers, OpenCV, Hugging Face Transformers, PyTorch/TensorFlow
Career Connection
Deep specialization makes you a targeted candidate for niche AI roles and research positions, offering a competitive edge.
Seek Industry Internships and Mentorship- (Semester 3)
Actively apply for internships at AI-centric companies or startups in India. Leverage IIT Roorkee''''s strong industry connections and alumni network for mentorship and opportunities to gain real-world experience.
Tools & Resources
Institute''''s Placement Cell, LinkedIn, Alumni networking events
Career Connection
Internships are critical for practical exposure, building professional networks, and often lead to pre-placement offers.
Participate in AI Competitions and Hackathons- (Semester 3)
Engage in national and international AI competitions, data science hackathons, and coding challenges. This sharpens problem-solving skills, provides exposure to diverse problems, and builds a public track record.
Tools & Resources
Kaggle, Analytics Vidhya, HackerEarth, Datathon platforms
Career Connection
Winning or performing well showcases exceptional talent and commitment, attracting recruiters and opening doors to selective roles.
Advanced Stage
Conduct High-Impact Dissertation Research- (Semester 3-4)
Choose a dissertation topic that addresses a challenging, novel problem in AI. Aim for publishable results in reputable conferences or journals, leveraging faculty guidance and advanced computing resources.
Tools & Resources
University HPC facilities, IEEE Xplore, ACM Digital Library, LaTeX for thesis writing
Career Connection
A strong dissertation can kickstart a research career, provide a foundation for PhD studies, or demonstrate advanced problem-solving skills to employers.
Focus on Placement-Oriented Skill Refinement- (Semester 4)
Dedicate time to mock interviews, resume building, and practicing coding challenges relevant to AI roles. Refine soft skills like communication, presentation, and teamwork through workshops and group projects.
Tools & Resources
Placement Cell workshops, Mock interview platforms, Career counseling services
Career Connection
Optimizes your chances for successful placements in leading tech companies and startups across India.
Build a Professional Online Presence- (Semester 4)
Maintain an updated LinkedIn profile highlighting skills, projects, and academic achievements. Create a personal website or blog to showcase your work, technical writings, and research interests, and engage with the AI community.
Tools & Resources
LinkedIn, GitHub Pages, Medium/Towards Data Science
Career Connection
Enhances visibility to recruiters, potential collaborators, and helps establish yourself as a thought leader in the Indian AI ecosystem.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science/ Information Technology or equivalent; M.C.A. OR M.Sc. in Computer Science/ Information Technology/ Mathematics/ Physics/ Statistics/ Electronics. Valid GATE score in CS/IT OR for sponsored candidates, as per Institute rules.
Duration: 4 semesters / 2 years
Credits: 55 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSN-501 | Data Structures and Algorithms for AI | Core | 4 | Asymptotic notations, Recurrences and sorting, Hashing and trees (BST, AVL, B-trees), Graph algorithms (BFS, DFS, shortest path), Dynamic Programming and Greedy Algorithms |
| CSN-502 | Machine Learning | Core | 4 | Linear models and SVMs, Decision Trees and ensemble methods, Clustering and dimensionality reduction (PCA), Neural Networks and deep learning basics, Bayesian learning and Reinforcement learning introduction |
| CSN-503 | Mathematical Foundations for AI | Core | 4 | Linear Algebra (vectors, matrices, eigenvalues), Probability Theory (random variables, distributions), Statistics (hypothesis testing, regression), Optimization (gradient descent, convex optimization), Calculus (derivatives, integrals, vector calculus) |
| HUT-501 | Research Methodology | Core | 2 | Research problem formulation, Literature review and gap analysis, Research design and data collection, Data analysis methods, Report writing and ethical considerations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSN-504 | Deep Learning | Core | 4 | Feedforward and Recurrent Neural Networks, Convolutional Neural Networks (CNNs), Transformers and Attention mechanisms, Generative Adversarial Networks (GANs), Autoencoders and Transfer Learning |
| CSN-505 | Natural Language Processing | Core | 4 | Text preprocessing and Language models, Word embeddings (Word2Vec, BERT), POS tagging and Named Entity Recognition, Machine Translation and Sentiment Analysis, Text Summarization and Information Extraction |
| CSN-506 | Computer Vision | Core | 4 | Image formation and Feature detection (SIFT, SURF), Image segmentation and Object recognition, Deep learning for vision (CNN architectures), Object detection and Tracking, Image captioning and Video analysis |
| CSN-5XX | Programme Elective I | Elective | 3 | Student selects from a pool of specialized electives in areas such as Reinforcement Learning, Probabilistic Graphical Models, Advanced Machine Learning, Human-Computer Interaction, Optimization Techniques for AI |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSN-601 | Reinforcement Learning | Core | 4 | Markov Decision Processes (MDPs), Dynamic Programming (Value, Policy Iteration), Monte Carlo and Temporal Difference learning, Q-learning and SARSA, Policy Gradient methods and Actor-Critic algorithms |
| CSN-6XX | Programme Elective II | Elective | 3 | Student selects from a pool of specialized electives in areas such as Multi-agent Systems, Machine Learning for Bioinformatics, Privacy and Security in AI, Robotics, Advanced Deep Learning Architectures |
| CSN-6XX | Programme Elective III | Elective | 3 | Student selects from a pool of specialized electives in areas such as Big Data Analytics, Cloud Computing for AI, Internet of Things, Explainable AI (XAI), Cognitive Computing |
| CSN-6XX | Programme Elective IV | Elective | 3 | Student selects from a pool of specialized electives in areas such as Medical Image Processing, Speech Processing, Data Mining, Federated Learning, Quantum Computing for AI |
| CSN-691 | Dissertation Part I | Project | 6 | Problem identification and definition, Literature survey and gap analysis, Research proposal and methodology, Initial data collection and model design, Progress reporting and presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSN-6XX | Programme Elective V | Elective | 3 | Student selects from a pool of specialized electives in areas such as Game Theory for AI, Evolutionary Computation, Time Series Analysis, Blockchain Technology, AI for Social Good |
| CSN-692 | Dissertation Part II | Project | 12 | Advanced model development and implementation, Extensive experimentation and result analysis, Comparative study and performance evaluation, Thesis writing and documentation, Presentation and defense of research findings |
