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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 knowledge in AI algorithms, machine learning models, deep learning architectures, and natural language processing. With India''''s rapidly growing tech sector, this program is designed to meet the escalating demand for skilled AI professionals, contributing to innovation across various industries.
Who Should Apply?
This program is ideal for engineering graduates from computer science, IT, electronics, and related disciplines, seeking entry into cutting-edge AI roles. It also caters to working professionals aiming to upskill and transition into specialized AI/ML domains, or researchers aspiring to contribute to advancements in intelligent systems within the Indian market.
Why Choose This Course?
Graduates of this program can expect diverse career paths such as AI Engineer, Data Scientist, Machine Learning Engineer, and Research Scientist in India. Entry-level salaries typically range from INR 6-10 lakhs per annum, growing significantly with experience. The program aligns with professional certifications from platforms like NASSCOM and provides a strong foundation for higher studies and R&D roles.
Student Success Practices
Foundation Stage
Master Mathematical and Algorithmic Foundations- (Semester 1-2)
Dedicate significant time to understanding linear algebra, probability, calculus, and advanced data structures. Use online platforms to practice coding algorithms weekly. Form study groups to solve complex mathematical problems and discuss theoretical concepts.
Tools & Resources
Khan Academy, MIT OpenCourseware, GeeksforGeeks, LeetCode, Mathematics for Machine Learning textbook
Career Connection
A strong foundation is critical for developing efficient and robust AI/ML models, which is highly valued by tech companies in India.
Develop Proficient Programming Skills in Python- (Semester 1-2)
Beyond course assignments, actively participate in coding challenges and build small projects using Python libraries like NumPy, Pandas, and Scikit-learn. Focus on writing clean, efficient, and well-documented code.
Tools & Resources
HackerRank, CodeChef, Kaggle kernels, DataCamp, Coursera courses on Python for Data Science
Career Connection
Python proficiency is a non-negotiable skill for AI/ML roles; early mastery ensures readiness for practical applications and industry projects.
Engage in Early Research Exploration- (Semester 1-2)
Attend department seminars, workshops, and interact with faculty about their research areas. Start reading introductory research papers in AI/ML to understand current trends and identify potential areas of interest for mini-projects.
Tools & Resources
arXiv, Google Scholar, specific research group websites at SRMIST, faculty office hours
Career Connection
Early exposure to research nurtures critical thinking and problem-solving skills, beneficial for both academic research and industry innovation roles in Indian R&D centers.
Intermediate Stage
Build a Strong Project Portfolio- (Semester 3)
Actively seek out opportunities for mini-projects and course projects that involve real-world datasets and problems. Document each project thoroughly, highlighting the problem statement, methodology, results, and your contributions. Focus on industry-relevant challenges.
Tools & Resources
GitHub, Kaggle competitions, open-source datasets (e.g., UCI Machine Learning Repository), industry hackathons
Career Connection
A robust portfolio demonstrates practical skills and problem-solving abilities to potential employers in India, significantly enhancing placement prospects.
Pursue Industry Internships- (Semester 3 (or summer after Sem 2))
Actively apply for internships during academic breaks or during academic semesters with startups, mid-sized tech companies, or R&D divisions of large corporations in India. Focus on gaining hands-on experience with industry-standard tools and workflows.
Tools & Resources
LinkedIn, Internshala, company career pages, SRMIST placement cell, networking events
Career Connection
Internships provide invaluable practical experience, industry contacts, and often lead to pre-placement offers, a common recruitment channel in India.
Specialize through Electives and Advanced Courses- (Semester 3)
Carefully choose electives that align with your career aspirations (e.g., Computer Vision, NLP, Reinforcement Learning, Data Science). Deepen your knowledge in these specialized areas through advanced online courses and relevant industry certifications.
Tools & Resources
NPTEL, deeplearning.ai courses (Coursera), edX, NVIDIA DLI, AWS/Azure AI certifications
Career Connection
Specialization makes you a more attractive candidate for specific roles and provides a competitive edge in the diverse Indian tech job market.
Advanced Stage
Excel in Capstone Project and Viva Voce- (Semester 4)
Dedicate thorough effort to the final project, ensuring it addresses a significant problem, showcases advanced AI/ML techniques, and has a measurable impact. Prepare meticulously for the comprehensive viva voce, demonstrating deep understanding of core and specialized subjects.
Tools & Resources
Research papers, technical journals, project mentors, mock viva sessions, comprehensive subject revisions
Career Connection
A well-executed project and confident viva performance are crucial for showcasing readiness for advanced R&D or industry roles and securing top placements.
Master Interview Skills and Networking- (Semester 3-4)
Actively participate in mock interviews, aptitude tests, and group discussions organized by the placement cell. Network with alumni and industry professionals through conferences, webinars, and professional platforms to explore career opportunities.
Tools & Resources
SRMIST placement cell, LinkedIn, Glassdoor, technical interview preparation books (e.g., Cracking the Coding Interview)
Career Connection
Strong interview skills and a professional network are vital for navigating the competitive Indian job market and securing desired roles.
Continuously Upskill with Latest AI Trends- (Throughout the program, intensified in Semester 4)
Stay updated with emerging AI/ML technologies, tools, and research breakthroughs by following prominent AI labs, journals, and tech news. Experiment with new frameworks and deploy small projects demonstrating these latest trends.
Tools & Resources
Towards Data Science, Synced, AI conferences (NeurIPS, ICML), Twitter (AI researchers), Google AI Blog
Career Connection
The AI field evolves rapidly; continuous learning ensures long-term career relevance and opens doors to innovative roles in leading tech companies.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in CSE/IT/SWE/ECE/EEE/EIE/ICE or MCA or M.Sc. (CS/IT) or equivalent degree with minimum 60% aggregate.
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 |
|---|---|---|---|---|
| MAD2101 | Advanced Data Structures and Algorithms | Core | 4 | Asymptotic Notations and Analysis, Trees and Heaps, Graphs and Graph Algorithms, Hashing Techniques, Dynamic Programming |
| MA2101 | Mathematical Foundations of Machine Learning | Core | 4 | Linear Algebra for ML, Probability and Statistics, Calculus and Optimization, Random Variables and Distributions, Eigenvalues and Eigenvectors |
| AD2101 | Advanced Machine Learning | Core | 4 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Ensemble Methods, Dimensionality Reduction, Model Evaluation and Selection |
| AD2102 | Advanced Machine Learning Lab | Lab | 2 | Python for ML with Libraries, Data Preprocessing and Visualization, Implementing Supervised Models, Implementing Unsupervised Models, Model Tuning and Evaluation |
| RM2101 | Research Methodology and IPR | Core | 3 | Fundamentals of Research, Research Design and Methods, Data Collection and Analysis, Report Writing and Ethics, Intellectual Property Rights |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AD2103 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers and Attention Mechanisms, Generative Adversarial Networks (GANs) |
| AD2104 | Natural Language Processing | Core | 4 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Sequence Models for NLP, Sentiment Analysis and Text Classification, Machine Translation and Text Generation |
| ADE21XX | Elective I (e.g., Computer Vision) | Elective | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Object Recognition and Detection, Image Segmentation, Deep Learning for Computer Vision |
| ADE21XX | Elective II (e.g., Reinforcement Learning) | Elective | 3 | Markov Decision Processes, Dynamic Programming, Q-Learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning |
| AD2105 | Deep Learning Lab | Lab | 2 | TensorFlow and Keras, PyTorch Framework, CNN Implementation, RNN and LSTM Implementation, NLP Task Implementation |
| AD2106 | Mini Project with Seminar | Project | 3 | Problem Identification and Literature Review, Project Design and Planning, Implementation and Testing, Technical Report Writing, Presentation and Viva Voce |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ADE21XX | Elective III (e.g., Data Science and Big Data Analytics) | Elective | 3 | Big Data Ecosystem, Hadoop and Spark Architecture, Data Warehousing Concepts, Data Visualization Tools, Predictive Analytics |
| ADE21XX | Elective IV (e.g., Ethical AI) | Elective | 3 | AI Bias and Fairness, Accountability and Transparency, Privacy in AI Systems, AI Safety and Control, Ethical Frameworks for AI |
| ADE21XX | Elective V (e.g., Explainable AI) | Elective | 3 | Need for Explainable AI, Interpretable Machine Learning Models, Post-hoc Explainability Techniques, Model Agnostic Methods, LIME and SHAP |
| AD21P1 | Project Work - Phase I | Project | 6 | Problem Identification and Formulation, Detailed Literature Survey, System Design and Architecture, Methodology Development, Preliminary Implementation and Report |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AD21P2 | Project Work - Phase II | Project | 12 | Full System Implementation, Extensive Testing and Evaluation, Results Analysis and Interpretation, Final Project Report, Demonstration and Presentation |
| AD2107 | Comprehensive Viva Voce | Core | 2 | Overall Subject Knowledge in AI/ML, Research Aptitude, Problem-Solving Skills, Communication and Presentation Skills, Understanding of Industry Trends |
