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M-TECH in Artificial Intelligence And Machine Learning at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Artificial Intelligence and Machine Learning at SRM Institute of Science and Technology Chengalpattu?
This Artificial Intelligence and Machine Learning program at SRM Institute of Science and Technology focuses on equipping students with advanced theoretical knowledge and practical skills in AI and ML domains. Given the rapid digital transformation in India, this program is designed to meet the growing demand for skilled AI professionals, distinguishing itself through a blend of foundational mathematics, core AI/ML techniques, and cutting-edge deep learning applications, directly addressing the evolving needs of the Indian tech industry.
Who Should Apply?
This program is ideal for engineering graduates, particularly from Computer Science, Information Technology, Electronics, and related fields, seeking entry into high-growth AI/ML roles. It also caters to working professionals with a foundational understanding of programming and data, looking to upskill or transition into specialized AI/ML engineering, data science, or research positions, enabling them to lead innovation in India''''s technology landscape.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Machine Learning Scientists, Data Scientists, Deep Learning Specialists, or NLP Engineers within Indian and global MNCs. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The program prepares students for roles in sectors like IT, healthcare, finance, and manufacturing, aligning with professional certifications from leading AI platforms and tools, fostering substantial growth trajectories.
Student Success Practices
Foundation Stage
Master Mathematical and Algorithmic Foundations- (Semester 1-2)
Dedicate significant time to thoroughly understand linear algebra, calculus, probability, and advanced data structures. These subjects are the bedrock of AI and ML. Actively solve problems from textbooks and online platforms to solidify concepts.
Tools & Resources
Khan Academy, NPTEL courses, GeeksforGeeks, LeetCode (for algorithms), Specialized textbooks
Career Connection
Strong mathematical and algorithmic skills are crucial for understanding, developing, and optimizing complex AI/ML models, directly impacting performance in technical interviews for R&D or engineering roles.
Develop Robust Programming Skills (Python & Libraries)- (Semester 1-2)
Beyond course assignments, continuously practice Python programming. Familiarize yourself deeply with key libraries like NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn. Work on mini-projects to apply learned concepts.
Tools & Resources
HackerRank, Kaggle (for beginner datasets), Jupyter Notebook, Official documentation for Python libraries
Career Connection
Proficiency in Python and its data science ecosystem is a fundamental requirement for almost all AI/ML engineering and data science roles in India, making you immediately employable.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and collaboratively solve problems. Teaching concepts to others reinforces your own understanding and exposes you to different perspectives.
Tools & Resources
Group study sessions, Online forums (Stack Overflow, Reddit communities for ML), Shared collaborative coding environments
Career Connection
Collaboration is vital in industry. Developing strong team communication and problem-solving skills through peer interaction enhances your appeal to employers and prepares you for collaborative projects.
Intermediate Stage
Pursue Specialization-Specific Projects and Internships- (Semester 2-3)
Actively seek out internships (summer/winter) in AI/ML related domains. Work on independent projects or contribute to faculty research that aligns with your interest, whether it''''s NLP, Computer Vision, or Reinforcement Learning.
Tools & Resources
LinkedIn, Internshala, SRMIST''''s career services, Research labs within the university
Career Connection
Internships provide real-world experience, build a strong professional network, and are often a direct pathway to pre-placement offers at Indian tech firms and startups.
Participate in Hackathons and Competitions- (Semester 2-3)
Regularly participate in AI/ML hackathons, Kaggle competitions, and other coding challenges. This sharpens problem-solving abilities, exposes you to diverse datasets, and helps build a portfolio.
Tools & Resources
Kaggle, Analytics Vidhya, Local college hackathons, Major tech company-sponsored competitions
Career Connection
Winning or performing well in competitions demonstrates practical skill and resilience, making your resume stand out to recruiters and showcasing your ability to apply theoretical knowledge under pressure.
Network with Industry Professionals and Alumni- (Semester 2-3)
Attend webinars, workshops, and industry conferences. Connect with alumni and professionals on LinkedIn. Informational interviews can provide insights into career paths and potential opportunities.
Tools & Resources
LinkedIn, Professional networking events (online/offline), University alumni network portals
Career Connection
Networking is crucial for discovering hidden job opportunities, gaining mentorship, and understanding industry trends, significantly aiding in placements and long-term career growth in India.
Advanced Stage
Excel in Capstone Project/Thesis Research- (Semester 3-4)
Dedicate yourself fully to your M.Tech project or thesis. Choose a topic that aligns with current industry demands or cutting-edge research. Aim for a publishable quality project or a functional prototype with significant impact.
Tools & Resources
Research papers (ArXiv, IEEE Xplore, ACM Digital Library), Specialized software/APIs, Collaboration with faculty advisors, University computing resources
Career Connection
A strong thesis project is your most significant portfolio piece for advanced R&D roles, PhD applications, or demonstrating expertise to potential employers in India and abroad.
Master Interview-Specific Skills & Technical Aptitude- (Semester 3-4)
Practice coding challenges (data structures, algorithms) extensively. Prepare for common AI/ML interview questions covering theoretical concepts, model understanding, and system design. Conduct mock interviews to refine your communication.
Tools & Resources
LeetCode, HackerRank, GeeksforGeeks, Glassdoor (for company-specific questions), Pramp (for mock interviews)
Career Connection
This direct preparation is indispensable for clearing technical rounds and securing high-paying placements in top AI/ML companies in India.
Build a Strong Online Portfolio and Personal Brand- (Semester 2-4 (Ongoing))
Curate your projects (code, reports, demos) on GitHub. Maintain an updated LinkedIn profile highlighting your skills, projects, and achievements. Consider a personal website or blog to showcase expertise and thought leadership.
Tools & Resources
GitHub, LinkedIn, Medium, Personal website builders (e.g., WordPress, GitHub Pages)
Career Connection
A strong online presence acts as a living resume, allowing recruiters to easily assess your capabilities and passion, significantly increasing your visibility and attractiveness for job opportunities.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science Engineering / Information Technology / Computer Science & Engineering with specialization in Artificial Intelligence & Machine Learning / Computer Science & Engineering with Specialization in Data Science / Software Engineering / Electrical & Electronics Engineering / Electronics & Communication Engineering / Electronics & Instrumentation Engineering / Mechatronics Engineering / equivalent degree with valid GATE score or SRMGEET (PG) score.
Duration: 2 years (4 semesters)
Credits: 71 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA2101 | Applied Probability and Statistical Methods | Core | 4 | Random variables and distributions, Joint Probability Distributions, Testing of Hypothesis, Analysis of Variance, Regression and Correlation |
| CS2101 | Advanced Data Structures and Algorithms | Core | 4 | Advanced Data Structures (Trees, Heaps), Graph Algorithms, Algorithm Design Techniques (Greedy, DP), Network Flow, Approximation Algorithms |
| CS2102 | Mathematical Foundations for Machine Learning | Core | 4 | Linear Algebra, Calculus and Optimization, Probability and Statistics for ML, Random Processes, Information Theory |
| CS2103 | Foundations of Artificial Intelligence | Core | 3 | Introduction to AI, Search Techniques, Knowledge Representation, Logic Programming, Uncertainty and Reasoning |
| CS2104 | Advanced Data Structures and Algorithms Lab | Lab | 2 | Implementation of Trees and Graphs, Algorithm design and analysis, Problem Solving with Data Structures, Dynamic Programming Applications |
| CS2105 | Foundations of Artificial Intelligence Lab | Lab | 2 | Implementation of Search Algorithms, Logic Programming (Prolog), AI problem solving using Python, Knowledge-based systems |
| CS21L1 | Professional Communication and Ethics | Soft Skill | 1 | Oral Communication, Written Communication, Presentation Skills, Professional Ethics, Report Writing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS2106 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning Basics, Model Evaluation and Selection, Ensemble Methods |
| CS2107 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation and Optimization, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) |
| CS2108 | Natural Language Processing | Core | 3 | Text Preprocessing and Tokenization, Language Models (N-grams, Word Embeddings), Syntactic and Semantic Analysis, Information Extraction, Machine Translation |
| CS2109 | Machine Learning Lab | Lab | 2 | Python for Machine Learning, Scikit-learn and Keras, Data Preprocessing and Visualization, Model Training and Evaluation, Implementing ML Algorithms |
| CS2110 | Deep Learning Lab | Lab | 2 | TensorFlow and PyTorch Implementation, CNN Architectures Implementation, RNN and LSTM Implementation, Transfer Learning Applications, Image and Text Data Processing |
| CS21E01 | Big Data Analytics | Elective | 3 | |
| CS21E06 | Explainable AI | Elective | 3 | |
| CS21L2 | Research Methodology and IPR | Soft Skill | 1 | Research Design, Data Analysis and Interpretation, Technical Report Writing, Intellectual Property Rights, Patent Filing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS2111 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming in RL, Monte Carlo and Temporal Difference Learning, Q-Learning and SARSA, Deep Reinforcement Learning |
| CS2112 | Computer Vision | Core | 4 | Image Processing Fundamentals, Feature Detection and Description, Object Recognition and Tracking, Image Segmentation, 3D Computer Vision |
| CS21E04 | Data Visualization | Elective | 3 | |
| CS21E07 | Edge AI | Elective | 3 | |
| CS21L3 | Project Phase I | Project | 6 | Problem Identification and Formulation, Literature Survey, Methodology Design, Initial Implementation and Prototyping, Project Proposal and Planning |
| CS21L4 | Technical Seminar | Seminar | 1 | Technical Topic Selection, Literature Review and Analysis, Presentation Skills, Question and Answer Handling, Seminar Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS21L5 | Project Phase II | Project | 12 | System Design and Development, Experimentation and Evaluation, Performance Analysis and Optimization, Result Interpretation and Discussion, Thesis/Dissertation Writing |
| CS21E10 | Advanced Deep Learning | Elective | 3 |
