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B-TECH in Artificial Intelligence at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Artificial Intelligence at SRM Institute of Science and Technology Chengalpattu?
This Artificial Intelligence program at SRM Institute of Science and Technology focuses on equipping students with advanced knowledge and skills in AI, Machine Learning, and Deep Learning. It integrates theoretical foundations with practical applications, addressing the growing demand for AI professionals in India''''s rapidly evolving tech landscape. The program emphasizes innovative problem-solving and ethical considerations in AI deployment, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for high school graduates with a strong foundation in Mathematics and Science, aspiring to innovate in AI. It also caters to individuals passionate about developing intelligent systems, machine learning models, and data-driven solutions for real-world problems. Freshers seeking a career in emerging technologies, research enthusiasts, and those looking to contribute to India''''s digital transformation will find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Data Scientists, Machine Learning Specialists, or NLP Developers in Indian IT giants and startups. Entry-level salaries typically range from INR 5-8 LPA, with significant growth potential up to INR 20+ LPA for experienced professionals. The curriculum supports pathways to higher education and research, aligning with industry certifications in AI and Data Science.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus on building a strong base in programming languages like C and Python, understanding data structures, and algorithms. Regularly practice coding challenges to solidify foundational logic.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, SRM''''s internal coding platforms
Career Connection
Strong programming fundamentals are essential for cracking technical interviews, building efficient AI/ML models, and excelling in core computer science roles.
Excel in Core Mathematics and Statistics- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics courses. These mathematical concepts are the bedrock for understanding AI and Machine Learning algorithms.
Tools & Resources
Khan Academy, NPTEL courses, reference textbooks, peer study groups
Career Connection
Crucial for understanding algorithm complexities, data analysis, and pursuing advanced research in artificial intelligence and related fields.
Participate in Mini-Projects and Workshops- (Semester 1-2)
Engage in small programming projects, even if they are simple, to apply theoretical knowledge. Attend departmental workshops on basic programming or software tools to gain practical skills.
Tools & Resources
GitHub for version control, departmental labs, open-source project communities
Career Connection
Develops practical problem-solving skills, enhances early portfolio building, and provides hands-on experience valued by Indian tech companies.
Intermediate Stage
Build a Strong AI/ML Portfolio- (Semester 3-5)
Actively work on Machine Learning and Deep Learning projects using real-world datasets. Implement algorithms from scratch and use popular libraries like TensorFlow and PyTorch.
Tools & Resources
Kaggle, Google Colab, TensorFlow, PyTorch, Scikit-learn, personal GitHub repository
Career Connection
A strong portfolio showcases practical skills and project experience to recruiters for internships and entry-level AI roles in India''''s competitive market.
Seek Industry Internships and Certifications- (Semester 4-5)
Apply for internships at AI/ML startups or established tech companies in India to gain practical experience. Pursue industry-recognized certifications in AI, Data Science, or cloud platforms.
Tools & Resources
LinkedIn, Internshala, Coursera, NASSCOM FutureSkills Prime, AWS/Azure/GCP AI certifications
Career Connection
Provides real-world experience, industry exposure, and a competitive edge, significantly improving placement chances in Indian and multinational companies.
Engage in AI/ML Research and Competitions- (Semester 3-5)
Participate in hackathons, coding competitions (e.g., Google Hash Code, ICPC), and AI/ML challenges. Explore research paper reading and group discussions to foster innovation.
Tools & Resources
Towards Data Science, arXiv, university research labs, competitive programming platforms
Career Connection
Fosters innovation, critical thinking, and advanced problem-solving, opening doors to R&D roles and demonstrating initiative to potential employers.
Advanced Stage
Specialize and Deepen Expertise- (Semester 6-8)
Choose electives strategically to specialize in areas like NLP, Computer Vision, Reinforcement Learning, or Ethical AI, aligning with your specific career goals and interests.
Tools & Resources
Advanced research papers, specialized libraries (e.g., OpenCV, spaCy), open-source projects in chosen sub-fields
Career Connection
Positions you as a subject matter expert, leading to specialized roles and potentially higher remuneration in niche AI domains within India.
Undertake Capstone Projects with Impact- (Semester 5-8)
Develop substantial, innovative projects (Project Phase I, II, III) addressing real-world problems, potentially with industry mentorship or a strong research focus.
Tools & Resources
Project management tools, collaborative coding platforms, faculty advisors, industry mentors
Career Connection
Creates a strong portfolio piece, demonstrates capability for complex problem-solving, and serves as a powerful talking point during placement interviews.
Network and Prepare for Placements- (Semester 7-8)
Attend industry seminars, tech conferences, and alumni meetups to build professional connections. Refine your resume, practice technical and HR interviews, and actively prepare for placement drives.
Tools & Resources
Professional networking events, career services department, mock interview platforms, LinkedIn
Career Connection
Maximizes your chances of securing top placements in leading AI companies and startups, and fosters long-term career growth in the Indian tech industry.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics/Biology/Biotechnology with a minimum aggregate percentage, typically 50-60%. Admission through SRMJEEE (SRM Joint Entrance Examination).
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEM101T | Communicative English | Foundation | 2 | Grammar and Vocabulary, Listening Skills, Speaking Skills, Reading Comprehension, Writing Skills, Presentation Skills |
| 21MAT101T | Calculus and Matrix Algebra | Foundation | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Matrices and Determinants, Vector Spaces, Linear Transformations |
| 21PHY101T | Engineering Physics | Foundation | 3 | Quantum Mechanics, Crystal Physics, Material Science, Laser Technology, Fiber Optics, Nanomaterials |
| 21PHT101L | Engineering Physics Lab | Lab | 1 | Spectrometer experiments, Ultrasound velocity, Compound pendulum, Resonance column, Photoelectric effect, Laser grating |
| 21CHE101T | Engineering Chemistry | Foundation | 3 | Water Treatment, Electrochemistry, Corrosion and its Control, Polymers and Composites, Fuels and Combustion, Energy Storage Devices |
| 21CHT101L | Engineering Chemistry Lab | Lab | 1 | Water quality analysis, Potentiometric titrations, Conductometric titrations, Viscosity measurements, Spectrophotometry, pH metric titrations |
| 21PCD101T | Programming for Problem Solving | Core | 3 | Programming Fundamentals, Data Types and Operators, Control Structures, Functions, Arrays and Strings, Pointers and Structures |
| 21PCD101L | Programming for Problem Solving Lab | Lab | 2 | Conditional and Looping statements, Functions implementation, Array and String manipulations, Pointers and Dynamic memory, Structures and Unions, File Handling |
| 21EEE101T | Basic Electrical and Electronics Engineering | Foundation | 3 | DC and AC Circuits, Electrical Machines, Diodes and Transistors, Operational Amplifiers, Digital Logic Gates, Transducers |
| 21GE101 | Engineering Graphics | Foundation | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Perspective Views, Computer Aided Drafting |
| 21PDL101L | Professional Skills and Development I | Skill Development | 1 | Self-Introduction, Goal Setting, Time Management, Basic Communication, Etiquette, Group Discussions |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEM201T | Advanced English | Foundation | 2 | Advanced Grammar, Business Communication, Report Writing, Technical Writing, Interpersonal Skills, Critical Reading |
| 21MAT201T | Probability and Statistics | Foundation | 4 | Probability Distributions, Random Variables, Statistical Inference, Hypothesis Testing, Regression Analysis, Correlation |
| 21EVS101T | Environmental Science and Engineering | Foundation | 3 | Ecosystems, Biodiversity, Pollution Control, Waste Management, Sustainable Development, Environmental Ethics |
| 21CSE201T | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms, Hashing |
| 21CSE201L | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching, Hashing Techniques |
| 21AIM201T | Object Oriented Programming and Design | Core | 3 | OOP Concepts (Classes, Objects), Inheritance, Polymorphism, Abstraction and Encapsulation, Exception Handling, UML Diagrams |
| 21AIM201L | Object Oriented Programming and Design Lab | Lab | 2 | Class and Object Implementation, Inheritance and Polymorphism, Abstract Classes and Interfaces, Exception Handling, File Operations in OOP, GUI Programming Basics |
| 21ECE201T | Digital Logic Design | Core | 3 | Number Systems, Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memories |
| 21ECE201L | Digital Logic Design Lab | Lab | 1 | Verification of Logic Gates, Boolean Function Simplification, Adders and Subtractors, Multiplexers and Demultiplexers, Flip-Flops, Counters and Registers |
| 21PDL201L | Professional Skills and Development II | Skill Development | 1 | Interview Skills, Resume Building, Presentation Techniques, Conflict Resolution, Emotional Intelligence, Critical Thinking |
| 21GE102 | Design Thinking | Foundation | 2 | Empathize, Define, Ideate, Prototype, Test, Innovation Process |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT301T | Discrete Mathematics | Core | 4 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| 21CSE301T | Computer Architecture and Organization | Core | 3 | CPU Organization, Memory Hierarchy, I/O Organization, Pipelining, Parallel Processing, Instruction Set Architecture |
| 21AIM301T | Database Management Systems | Core | 3 | Database Models, SQL Queries, Database Design (ER, Normalization), Transaction Management, Concurrency Control, Database Security |
| 21AIM301L | Database Management Systems Lab | Lab | 2 | DDL and DML Commands, Joins and Subqueries, Stored Procedures and Functions, Database Connectivity (JDBC/ODBC), Schema Design, Transaction Control |
| 21AIM302T | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Deadlocks |
| 21AIM302L | Operating Systems Lab | Lab | 2 | Shell Programming, Process Creation and Management, CPU Scheduling Algorithms, Memory Allocation Strategies, Deadlock Detection and Prevention, File System Calls |
| 21AIM303T | Fundamentals of Artificial Intelligence | Specialization Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms (DFS, BFS, A*), Knowledge Representation, Logical Reasoning, Introduction to Machine Learning |
| 21AIM303L | Fundamentals of Artificial Intelligence Lab | Lab | 2 | Implementing Search Algorithms, Constraint Satisfaction Problems, Prolog Programming, Game Playing Algorithms, Decision Trees, Knowledge Representation Systems |
| 21AIM304T | Python Programming for AI | Specialization Core | 3 | Python Basics, Data Structures in Python, Functions and Modules, Object-Oriented Python, File Handling, Introduction to Libraries (Numpy, Pandas) |
| 21AIM304L | Python Programming for AI Lab | Lab | 2 | Python scripting, Data manipulation with Pandas, Numerical computing with NumPy, Data visualization with Matplotlib, Web scraping basics, Basic Machine Learning using Scikit-learn |
| 21PDL301L | Professional Skills and Development III | Skill Development | 1 | Advanced Communication, Interpersonal Skills, Team Work and Collaboration, Leadership Basics, Stress Management, Problem Solving |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT401T | Applied Linear Algebra | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Matrix Decompositions, Singular Value Decomposition, Applications in Machine Learning |
| 21AIM401T | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis (Time, Space Complexity), Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| 21AIM401L | Design and Analysis of Algorithms Lab | Lab | 2 | Implementation of Divide and Conquer, Dynamic Programming Solutions, Greedy Algorithms, Graph Traversal and Shortest Path, Minimum Spanning Trees, Network Flow Problems |
| 21AIM402T | Machine Learning | Specialization Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation, Ensemble Methods |
| 21AIM402L | Machine Learning Lab | Lab | 2 | Implementing Regression Models, Classification with SVM, Decision Trees, Clustering (K-Means, Hierarchical), Dimensionality Reduction (PCA), Cross-Validation, Using Scikit-learn for ML tasks |
| 21AIM403T | Natural Language Processing | Specialization Core | 3 | Text Preprocessing, Tokenization and Stemming, Part-of-Speech Tagging, Syntactic Parsing, Semantic Analysis, Machine Translation |
| 21AIM403L | Natural Language Processing Lab | Lab | 2 | Text Cleaning and Normalization, N-gram Models, Named Entity Recognition, Sentiment Analysis, Text Summarization, Word Embeddings |
| 21AIM404T | Data Science and Big Data Analytics | Specialization Core | 3 | Introduction to Data Science, Data Preprocessing, Exploratory Data Analysis, Big Data Technologies (Hadoop, Spark), Data Visualization, Data Pipelines |
| 21AIM404L | Data Science and Big Data Analytics Lab | Lab | 2 | Data manipulation with R/Python, SQL for Data Analysis, Data Cleaning techniques, Big Data platform setup (local), Data visualization tools, Introduction to Spark/Hadoop ecosystem |
| 21PDL401L | Professional Skills and Development IV | Skill Development | 1 | Negotiation Skills, Decision Making, Entrepreneurial Mindset, Digital Literacy, Ethical Hacking Awareness, Cybersecurity Basics |
| 21GE103 | Indian Constitution | Foundation | 1 | Constituent Assembly, Preamble, Fundamental Rights, Directive Principles, Union and State Governments, Local Self-Government |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM501T | Deep Learning | Specialization Core | 3 | Neural Networks Fundamentals, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Reinforcement Learning |
| 21AIM501L | Deep Learning Lab | Lab | 2 | TensorFlow/PyTorch Basics, Building CNN for Image Classification, Implementing RNN for Sequence Data, Hyperparameter Tuning, Transfer Learning, Working with GPUs |
| 21AIM502T | Computer Vision | Specialization Core | 3 | Image Formation, Image Processing Basics, Feature Detection and Extraction, Object Recognition, Image Segmentation, 3D Vision |
| 21AIM502L | Computer Vision Lab | Lab | 2 | OpenCV for Image Manipulation, Edge Detection, Image Segmentation Algorithms, Object Detection using YOLO/SSD, Facial Recognition, Image Stitching |
| 21AIM503T | Reinforcement Learning | Specialization Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Q-Learning and SARSA, Deep Reinforcement Learning |
| 21AIM503L | Reinforcement Learning Lab | Lab | 2 | Implementing MDPs, Q-Learning Algorithms, SARSA Algorithm, Policy Gradient Methods, OpenAI Gym Environments, Deep Q-Networks (DQNs) |
| 21AIMET | AI Elective I | Elective | 3 | Topics are chosen from a pool of available AI electives based on student interest., Examples include Robotics, Explainable AI, Cognitive Computing, Big Data for AI. |
| 21AIMEL | AI Elective Lab I | Elective Lab | 2 | Practical implementation related to the chosen AI Elective I., Project work or case studies in the elective domain. |
| 21AIMPRO | Project Phase I | Project | 3 | Problem Identification, Literature Survey, Requirement Analysis, System Design (Preliminary), Project Proposal, Feasibility Study |
| 21PDL501L | Professional Skills and Development V | Skill Development | 1 | Entrepreneurship, Market Research, Business Plan Development, Intellectual Property Rights, Innovation Strategies, Startup Ecosystem |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM601T | Ethics in AI | Specialization Core | 3 | Ethical Principles in AI, Bias and Fairness in AI, AI and Privacy, Accountability and Transparency, Societal Impact of AI, Regulations and Governance of AI |
| 21AIM602T | Cloud Computing for AI | Specialization Core | 3 | Cloud Computing Basics, IaaS, PaaS, SaaS, Cloud Deployment Models, AI Services on AWS/Azure/GCP, Serverless Computing for AI, Data Storage and Processing in Cloud |
| 21AIM602L | Cloud Computing for AI Lab | Lab | 2 | Setting up cloud environments, Deploying ML models on cloud, Using cloud AI APIs (Vision, NLP), Managing data in cloud storage, Serverless function deployment, Containerization for AI applications |
| 21AIMET | AI Elective II | Elective | 3 | Topics are chosen from a pool of available AI electives based on student interest., Examples include AI for Cyber Security, Quantum AI, Neuro-Linguistic Programming (NLP) Applications. |
| 21AIMEL | AI Elective Lab II | Elective Lab | 2 | Practical implementation related to the chosen AI Elective II., Advanced programming tasks or mini-projects. |
| 21AIMINT | Industrial Internship | Internship | 6 | Real-world project experience, Application of theoretical knowledge, Industry standard tools and practices, Professional networking, Report writing and presentation, Problem-solving in an industrial setting |
| 21GE104 | Professional Ethics | Foundation | 1 | Human Values, Engineering Ethics, Ethical Theories, Code of Ethics, Safety, Risks, and Liability, Global Issues |
| 21PDL601L | Professional Skills and Development VI | Skill Development | 1 | Corporate Etiquette, Global Work Culture, Project Management Basics, Financial Literacy, Sustainable Practices, Advanced Resume Writing |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM701T | AI for Robotics | Specialization Core | 3 | Robot Kinematics, Robot Dynamics, Robot Control Architectures, Path Planning, Robot Vision, Human-Robot Interaction |
| 21AIM701L | AI for Robotics Lab | Lab | 2 | Robot Programming (ROS), Kinematic and Dynamic Simulations, Robot Control Algorithms, Sensor Integration, Navigation and Localization, Robot manipulation tasks |
| 21AIMET | AI Elective III | Elective | 3 | Topics are chosen from a pool of available AI electives based on student interest., Examples include Speech and Audio Processing, AI for Healthcare, Industrial AI. |
| 21AIMEL | AI Elective Lab III | Elective Lab | 2 | Practical implementation related to the chosen AI Elective III., Advanced research-oriented projects or simulations. |
| 21AIMPRO | Project Phase II | Project | 6 | Detailed Design and Implementation, Module Development, Testing and Debugging, Data Collection and Analysis, Intermediate Report Submission, Proof of Concept Demonstration |
| 21VET | Value Added Elective | Elective | 3 | Topics are chosen from a university-wide pool of value-added electives., Examples include soft skills, foreign languages, or interdisciplinary topics. |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIMET | AI Elective IV | Elective | 3 | Topics are chosen from a pool of available AI electives based on student interest., Examples include Edge AI, Cognitive Robotics, AI for Autonomous Systems. |
| 21AIMEL | AI Elective Lab IV | Elective Lab | 2 | Practical implementation related to the chosen AI Elective IV., Final project development and integration. |
| 21AIMPRO | Project Phase III | Project | 8 | System Integration, Performance Evaluation, Optimization and Refinement, Final Report Preparation, Project Demonstration, Technical Presentation and Viva-Voce |
| 21OPET | Open Elective | Elective | 3 | Topics are chosen from a university-wide pool of open electives across various disciplines., Examples include management, humanities, or advanced science topics. |
