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B-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 B.Tech Artificial Intelligence and Machine Learning program at SRM Institute of Science and Technology focuses on equipping students with advanced knowledge and practical skills in AI, ML, and their applications. It emphasizes robust theoretical foundations alongside hands-on experience, preparing graduates for the rapidly evolving Indian tech industry. The curriculum covers core aspects from data science to deep learning, catering to the significant demand for AI professionals.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and programming seeking entry into high-growth tech domains. It also suits working professionals aiming to upskill in AI/ML or career changers transitioning into data-driven roles. Specific prerequisites include a solid 10+2 academic background in Physics, Chemistry, and Mathematics, reflecting the analytical rigor required.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Machine Learning Scientists, Data Scientists, or NLP Specialists within leading Indian and international companies. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly higher. Growth trajectories are steep, with opportunities to lead AI innovation in sectors like finance, healthcare, and e-commerce, aligning with India''''s digital transformation.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate consistent time to mastering Python programming and data structures. Practice coding problems daily on platforms like HackerRank or LeetCode to build logical thinking and problem-solving skills, which are crucial for subsequent AI/ML courses.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Jupyter Notebooks
Career Connection
Strong programming skills are the bedrock for any AI/ML role, enabling efficient algorithm implementation and data handling, directly impacting eligibility for core tech placements.
Build a Strong Mathematical Base- (Semester 1-3)
Focus intensely on Calculus, Linear Algebra, Probability, and Statistics. These mathematical concepts are fundamental to understanding how AI and ML algorithms work. Utilize online courses like Khan Academy or NPTEL to supplement classroom learning and clarify complex topics.
Tools & Resources
Khan Academy, NPTEL, MIT OpenCourseware, Schaum''''s Outlines
Career Connection
A robust mathematical understanding differentiates candidates, particularly for research-oriented or advanced ML roles, allowing for deeper comprehension and innovation in algorithm design.
Participate in Coding & Logic Challenges- (Semester 1-2)
Engage in college-level coding competitions and logical reasoning challenges. These activities enhance competitive programming skills, foster teamwork, and provide exposure to diverse problem sets, improving aptitude for technical interviews.
Tools & Resources
CodeChef, TopCoder, College Coding Clubs
Career Connection
Participation demonstrates initiative and problem-solving prowess to recruiters, often leading to direct internship or job interview opportunities, especially with product-based companies.
Intermediate Stage
Undertake Mini-Projects and Kaggle Competitions- (Semester 3-5)
Apply theoretical knowledge by working on small-scale AI/ML projects. Participate in Kaggle competitions to gain hands-on experience with real-world datasets, different machine learning models, and team collaboration. Document all projects on GitHub.
Tools & Resources
Kaggle, GitHub, Google Colab, Scikit-learn
Career Connection
A strong project portfolio is vital for showcasing practical skills during internships and placements, making you a more attractive candidate for Data Scientist and ML Engineer roles.
Pursue Domain-Specific Certifications- (Semester 4-6)
Beyond core curriculum, pursue certifications in specialized areas like Deep Learning (e.g., Coursera''''s Deep Learning Specialization by Andrew Ng) or specific cloud AI/ML platforms (AWS Machine Learning Specialty, Azure AI Engineer).
Tools & Resources
Coursera, edX, Udemy, AWS Training & Certification, Microsoft Learn
Career Connection
Certifications validate specialized knowledge, making candidates stand out for niche roles and demonstrating a commitment to continuous learning in a fast-evolving field.
Network with Industry Professionals- (Semester 3-5)
Attend industry workshops, webinars, and tech meetups (both online and offline). Connect with professionals on LinkedIn, seeking mentorship and insights into industry trends and job opportunities in the Indian AI ecosystem.
Tools & Resources
LinkedIn, Meetup.com, Industry Conferences (e.g., Data Science Congress India)
Career Connection
Networking opens doors to referrals, internship leads, and valuable career advice, significantly improving chances of securing desired roles and understanding industry expectations.
Advanced Stage
Engage in Research or Advanced Projects- (Semester 6-8)
Collaborate with faculty on research projects, aiming for publication in conferences or journals, or work on a significant capstone project (e.g., during Project Work II/III) that solves a real-world problem, potentially leading to a patent or startup idea.
Tools & Resources
Research papers (arXiv, IEEE Xplore), University Research Labs, Faculty Mentors
Career Connection
High-impact projects and research publications enhance your profile for R&D roles, academic pursuits, and demonstrate innovation and problem-solving capabilities to top-tier companies.
Intensive Placement Preparation- (Semester 7-8)
Participate in mock interviews, group discussions, and aptitude tests organized by the university''''s placement cell. Focus on revising core AI/ML concepts, data structures, algorithms, and behavioral questions to ace company-specific recruitment drives.
Tools & Resources
SRMIST Placement Cell, Glassdoor, GeeksforGeeks Interview Prep, Mock Interview Platforms
Career Connection
Thorough preparation directly translates into securing placements with desired companies, often resulting in higher salary packages and better career starting points.
Build a Professional Online Presence- (Semester 6-8)
Curate a professional LinkedIn profile, maintain an active GitHub repository with all projects, and potentially create a personal website/blog to showcase skills, projects, and thought leadership. This acts as a digital portfolio for recruiters.
Tools & Resources
LinkedIn, GitHub, WordPress/Wix for personal websites
Career Connection
A strong online presence makes you discoverable to recruiters and demonstrates professionalism and a proactive approach to career development, enhancing job prospects.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics (or Biology/Biotechnology for specific programs) with a minimum aggregate percentage as per university norms.
Duration: 4 years / 8 semesters
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18LEH101T | Communicative English | Core | 3 | Grammar and Vocabulary, Reading and Comprehension, Writing Skills, Speaking Skills, Effective Communication |
| 18MAB101T | Calculus and Linear Algebra | Core | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Vector Spaces, Linear Transformations |
| 18BPH101T | Physics | Core | 3 | Quantum Mechanics, Solid State Physics, Semiconductor Physics, Optics, Materials Science |
| 18BCH101T | Chemistry | Core | 3 | Electrochemistry, Corrosion, Water Technology, Spectroscopy, Polymer Chemistry |
| 18CSE101J | Programming in Python | Core | 3 | Python Basics, Data Structures in Python, Control Flow, Functions and Modules, Object-Oriented Programming |
| 18AIM101T | Introduction to AI & ML | Core | 3 | Introduction to AI, Problem Solving, Machine Learning Basics, AI Applications, Ethical AI |
| 18LEL101L | Communicative English Lab | Lab | 1 | Listening and Speaking Practice, Presentation Skills, Group Discussions, Interview Skills, Public Speaking |
| 18BPL101L | Physics Lab | Lab | 1 | Basic Physics Experiments, Optical Phenomena, Semiconductor Characteristics, Electrical Measurements, Magnetic Field Studies |
| 18BCL101L | Chemistry Lab | Lab | 1 | Volumetric Analysis, Chemical Kinetics, Water Quality Testing, pH Measurements, Conductometry |
| 18CSE101L | Python Programming Lab | Lab | 1 | Python Program Execution, Conditional Statements, Looping Constructs, Functions Implementation, File Handling |
| 18AIM101L | Introduction to AI & ML Lab | Lab | 1 | AI Problem Solving Tools, ML Algorithm Implementation, Data Preprocessing, Model Evaluation, Simple AI Projects |
| 18PDT101J | Professional Skills Training | Core | 1 | Career Planning, Personality Development, Soft Skills, Time Management, Communication Ethics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB102T | Advanced Calculus and Complex Analysis | Core | 4 | Partial Differentiation, Multiple Integrals, Vector Calculus, Complex Numbers, Analytic Functions |
| 18BEE101J | Basic Electrical and Electronics Engineering | Core | 3 | DC/AC Circuits, Semiconductor Devices, Digital Logic, Microcontrollers, Sensors and Actuators |
| 18CSB101J | Data Structures | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Sorting Algorithms, Searching Algorithms |
| 18CSC101T | Computer Architecture and Organization | Core | 3 | CPU Organization, Memory Hierarchy, I/O Organization, Instruction Set, Pipelining |
| 18CSA101T | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, Concurrency, Deadlocks |
| 18CSL101L | Data Structures Lab | Lab | 1 | Implementation of Linked Lists, Stack/Queue Operations, Tree Traversals, Graph Algorithms, Sorting/Searching Implementation |
| 18CSL102L | Operating Systems Lab | Lab | 1 | Linux Commands, Shell Scripting, Process Creation, Inter-Process Communication, System Calls |
| 18PDT102J | Professional Skills Training | Core | 1 | Goal Setting, Stress Management, Emotional Intelligence, Creative Thinking, Teamwork |
| 18IDD101J | Design Thinking | Core | 1 | Empathize, Define, Ideate, Prototype, Test |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB201T | Probability and Statistics | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis |
| 18CSB201J | Database Management Systems | Core | 3 | Relational Model, SQL Queries, Normalization, Transaction Management, Database Security |
| 18AIM201T | Data Analysis and Visualization | Core | 3 | Data Preprocessing, Exploratory Data Analysis, Data Cleaning, Data Visualization Techniques, Statistical Graphics |
| 18AIM202T | Machine Learning Fundamentals | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation Metrics |
| 18AIM203T | Artificial Intelligence Principles | Core | 3 | Intelligent Agents, Search Algorithms, Knowledge Representation, Logical Reasoning, Game Playing |
| 18CSL201L | Database Management Systems Lab | Lab | 1 | DDL and DML Commands, Advanced SQL, PL/SQL Programming, Database Connectivity, Mini Project |
| 18AIM201L | Data Analysis and Visualization Lab | Lab | 1 | Python for Data Analysis (Pandas, NumPy), Data Visualization (Matplotlib, Seaborn), Data Cleaning Tools, Interactive Dashboards, Case Studies |
| 18AIM202L | Machine Learning Fundamentals Lab | Lab | 1 | Scikit-learn, Linear Regression, Logistic Regression, Decision Trees, Clustering Algorithms |
| 18PDT201J | Professional Skills Training | Core | 1 | Resume Building, Interview Preparation, Verbal Reasoning, Quantitative Aptitude, Analytical Skills |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MAB202T | Discrete Mathematics and Graph Theory | Core | 4 | Set Theory, Mathematical Logic, Combinatorics, Graph Theory, Recurrence Relations |
| 18CSB202J | Design and Analysis of Algorithms | Core | 3 | Algorithm Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 18AIM204T | Deep Learning | Core | 3 | Neural Networks, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models |
| 18AIM205T | Natural Language Processing | Core | 3 | Text Preprocessing, Tokenization, Syntactic Analysis, Semantic Analysis, Machine Translation |
| 18AIM206T | Computer Vision | Core | 3 | Image Processing Basics, Feature Detection, Object Recognition, Image Segmentation, Video Analysis |
| 18CSL202L | Design and Analysis of Algorithms Lab | Lab | 1 | Algorithm Implementation, Time Complexity Analysis, Space Complexity Analysis, Practical Algorithm Design, Problem Solving |
| 18AIM203L | Deep Learning Lab | Lab | 1 | TensorFlow/Keras, PyTorch, CNN Implementation, RNN Implementation, Deep Learning Projects |
| 18AIM204L | Natural Language Processing Lab | Lab | 1 | NLTK, SpaCy, Text Classification, Sentiment Analysis, Named Entity Recognition, Chatbot Development |
| 18PDT202J | Professional Skills Training | Core | 1 | Critical Thinking, Problem Solving Strategies, Decision Making, Negotiation Skills, Cultural Intelligence |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18GEA001J | Professional Ethics and Human Values | Core | 3 | Engineering Ethics, Moral Philosophy, Human Values, Corporate Social Responsibility, Environmental Ethics |
| 18CSB301J | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Project Management |
| 18AIM301T | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Deep Reinforcement Learning |
| 18AIM302T | Big Data Analytics | Core | 3 | Big Data Technologies (Hadoop, Spark), Distributed Computing, NoSQL Databases, Data Stream Processing, Big Data Machine Learning |
| 18AIM303T | Cognitive Computing | Core | 3 | Cognitive Architectures, Knowledge Representation, Reasoning and Inference, Perception Systems, Human-Computer Interaction |
| 18AIM3XXT | Professional Elective I | Professional Elective | 3 | Selected advanced topic as per elective choice |
| 18CSL301L | Software Engineering Lab | Lab | 1 | UML Diagrams, Software Design Patterns, Version Control (Git), Testing Frameworks, Agile Methodologies |
| 18AIM301L | Reinforcement Learning Lab | Lab | 1 | OpenAI Gym, Q-Learning, Policy Gradient Methods, Actor-Critic Models, RL Projects |
| 18AIM302L | Big Data Analytics Lab | Lab | 1 | Hadoop Ecosystem (HDFS, MapReduce), Spark Programming, Hive, Pig, Cassandra/MongoDB, Big Data Case Studies |
| 18PDT301J | Professional Skills Training | Core | 1 | Group Discussion Techniques, Personal Interview Skills, Mock Interviews, Presentation Mastery, Workplace Etiquette |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18AIM304T | AI Ethics and Governance | Core | 3 | Ethical AI Principles, Fairness and Bias, Accountability and Transparency, Privacy in AI, AI Regulations and Policies |
| 18AIM305T | Cloud for AI/ML | Core | 3 | Cloud Computing Basics, AWS/Azure/GCP for ML, Serverless ML, MLOps, Containerization (Docker, Kubernetes) |
| 18AIM3XXT | Professional Elective II | Professional Elective | 3 | Selected advanced topic as per elective choice |
| 18AIM3XXT | Professional Elective III | Professional Elective | 3 | Selected advanced topic as per elective choice |
| 18XXX001T | Open Elective I | Open Elective | 3 | Chosen interdisciplinary topic |
| 18AIM303L | Cloud for AI/ML Lab | Lab | 1 | Cloud VM Setup, ML Services Deployment, Data Storage on Cloud, Containerizing ML Models, CI/CD for ML |
| 18AIM399J | Project Work I | Project | 3 | Problem Identification, Literature Survey, System Design, Implementation, Report Writing |
| 18PDT302J | Professional Skills Training | Core | 1 | Industry Trends Awareness, Entrepreneurial Skills, Intellectual Property Rights, Sustainable Development, Leadership Principles |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18AIM401T | Internet of Things for AI/ML | Core | 3 | IoT Architecture, IoT Sensors and Actuators, Edge AI, Fog Computing, IoT Data Analytics |
| 18AIM4XXT | Professional Elective IV | Professional Elective | 3 | Selected advanced topic as per elective choice |
| 18AIM4XXT | Professional Elective V | Professional Elective | 3 | Selected advanced topic as per elective choice |
| 18XXX002T | Open Elective II | Open Elective | 3 | Chosen interdisciplinary topic |
| 18AIM499J | Project Work II / Internship | Project / Internship | 6 | Advanced Project Development, Industrial Problem Solving, Team Collaboration, Deployment Strategies, Technical Report & Presentation |
| 18PDT401J | Professional Skills Training | Core | 1 | Advanced Communication, Networking Skills, Innovation Management, Start-up Ecosystem, Global Trends in AI |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18AIM4XXT | Professional Elective VI | Professional Elective | 3 | Selected advanced topic as per elective choice |
| 18AIM4XXT | Professional Elective VII | Professional Elective | 3 | Selected advanced topic as per elective choice |
| 18AIM498J | Project Work III / Capstone Project | Project | 6 | Real-world Problem Solving, End-to-end System Development, Research Methodology, Advanced Analytics, Patent Filing/Publication |
