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B-TECH in Computer Science And Engineering With Artificial Intelligence And Machine Learning at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science and Engineering with Artificial Intelligence and Machine Learning at SRM Institute of Science and Technology Chengalpattu?
This B.Tech in Computer Science and Engineering (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 and ML domains. It emphasizes theoretical foundations alongside hands-on application, addressing the growing demand for skilled professionals in India''''s rapidly evolving tech landscape. The program uniquely integrates core CSE principles with cutting-edge AI techniques, preparing graduates to tackle complex real-world challenges. It fosters an innovative learning environment, aligned with contemporary industry requirements.
Who Should Apply?
This program is ideal for ambitious fresh graduates seeking entry into the thriving fields of artificial intelligence, machine learning, and data science. It also caters to working professionals aiming to upskill and specialize in AI/ML, enhancing their career prospects. Furthermore, it welcomes career changers transitioning into the tech industry with a strong foundation in computer science or related quantitative disciplines, providing them with specialized expertise needed for high-demand roles. A keen interest in problem-solving and algorithmic thinking is a prerequisite.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths such as AI Engineer, Machine Learning Scientist, Data Scientist, NLP Engineer, and Robotics Engineer in top Indian and multinational companies. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning upwards of INR 15-30+ LPA, depending on skills and company. The program aligns with industry certifications like TensorFlow Developer or Azure AI Engineer, offering significant growth trajectories in areas like autonomous systems, healthcare AI, and intelligent automation.
Student Success Practices
Foundation Stage
Build Strong Programming Foundations- (Semester 1-2)
Dedicate significant time to mastering programming logic and data structures using C and Python. Actively solve problems on coding platforms to build confidence and develop efficient algorithmic thinking from the very first semester.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, C programming books
Career Connection
Essential for clearing technical rounds in placements and for building complex AI/ML models in later stages.
Engage in STEM Clubs and Societies- (Semester 1-2)
Join technical clubs focused on coding, robotics, or innovation. Participate in inter-college coding competitions, hackathons, and project exhibitions to apply theoretical knowledge, collaborate with peers, and showcase early projects.
Tools & Resources
College technical clubs (e.g., CodeChef Chapter, AI/ML Interest Group), Local hackathons
Career Connection
Develops teamwork, problem-solving skills, and a strong project portfolio, which are highly valued by recruiters.
Focus on Core Engineering Mathematics- (Semester 1-2)
Pay close attention to Engineering Mathematics I and II, as a strong grasp of calculus, linear algebra, and probability is fundamental for advanced AI and Machine Learning concepts. Seek extra help or join study groups if needed.
Tools & Resources
NPTEL courses, Khan Academy, Textbook examples, Peer study groups
Career Connection
Provides the mathematical backbone necessary to understand and innovate in complex AI/ML algorithms and research, crucial for higher studies and R&D roles.
Intermediate Stage
Dive Deep into AI/ML Fundamentals- (Semester 3-5)
Beyond coursework, explore online specialized courses and MOOCs in AI and Machine Learning. Work on mini-projects leveraging Python libraries like scikit-learn, TensorFlow, or PyTorch to gain practical experience.
Tools & Resources
Coursera, Udacity, Kaggle datasets, TensorFlow/PyTorch documentation, Google Colab
Career Connection
Builds a strong practical portfolio, differentiates you in interviews, and prepares you for advanced specialization.
Seek Industry Mentorship and Internships- (Semester 3-5)
Actively network with industry professionals through LinkedIn, alumni connections, and college career fairs. Secure internships during summer breaks in AI/ML roles to gain real-world exposure and understand industry best practices.
Tools & Resources
LinkedIn, SRMIST Alumni Network, College placement cell, Internship portals (Internshala, LetsIntern)
Career Connection
Provides invaluable industry experience, helps refine career goals, and often leads to pre-placement offers (PPOs).
Contribute to Open Source AI/ML Projects- (Semester 3-5)
Start contributing to open-source projects on platforms like GitHub related to AI, machine learning, or data science. This demonstrates collaborative skills, code quality, and a proactive learning attitude.
Tools & Resources
GitHub, GitLab, Stack Overflow, AI/ML communities
Career Connection
Showcases practical coding ability, teamwork, and commitment to learning, impressing potential employers and building a public profile.
Advanced Stage
Undertake a Significant AI/ML Capstone Project- (Semester 7-8)
Collaborate with faculty or industry partners on a substantial AI/ML project during your final year. Focus on solving a real-world problem, apply advanced algorithms, and ensure measurable outcomes.
Tools & Resources
Research papers, Industry problem statements, Faculty guidance, High-performance computing resources
Career Connection
Forms the cornerstone of your portfolio, demonstrating specialized expertise and readiness for challenging roles, often directly leading to job opportunities or research pursuits.
Prepare Rigorously for Placements & Higher Studies- (Semester 6-8)
Start placement preparation early, focusing on technical interviews, aptitude tests, and soft skills. If pursuing higher studies, prepare for GRE/GATE and work on strong recommendation letters and Statement of Purpose.
Tools & Resources
Placement training modules, Mock interviews, Online aptitude tests, Career counseling
Career Connection
Maximizes chances for securing top-tier placements in core AI/ML companies or gaining admission to prestigious graduate programs globally.
Specialize and Certify in Niche AI/ML Areas- (Semester 6-8)
Identify a niche area within AI/ML (e.g., computer vision, NLP, MLOps, explainable AI) and gain deeper expertise through advanced electives, workshops, and professional certifications from platforms like NVIDIA, AWS, or Google.
Tools & Resources
Professional certifications (e.g., AWS Certified Machine Learning Specialist, Google AI Engineer), Advanced research papers, Specialized workshops
Career Connection
Positions you as an expert in a specific, high-demand segment of AI/ML, opening doors to highly specialized and well-compensated roles.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 4 years / 8 semesters
Credits: 169 Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23HS101 | English for Engineers | Core | 3 | Communication Skills, Technical Writing, Listening & Speaking, Vocabulary & Grammar, Presentation Skills |
| 23MA101 | Engineering Mathematics I | Core | 4 | Matrices, Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus |
| 23PH101 | Engineering Physics | Core | 3 | Quantum Physics, Optics, Solid State Physics, Materials Science, Nanotechnology |
| 23CS101 | Problem Solving using C Programming | Core | 3 | C Language Fundamentals, Control Flow, Functions, Arrays & Strings, Pointers, Structures & Unions |
| 23EE101 | Basic Electrical and Electronics Engineering | Core | 3 | DC & AC Circuits, Semiconductor Devices, Diodes & Transistors, Digital Electronics Basics, Operational Amplifiers |
| 23CS181 | Problem Solving using C Programming Laboratory | Lab | 1.5 | C Programming Exercises, Debugging, Problem Solving, Data Structures Implementation, Algorithm Design |
| 23PH181 | Engineering Physics Laboratory | Lab | 1.5 | Experimental Physics, Data Analysis, Measurement Techniques, Optics Experiments, Electronic Circuits |
| 23PD101 | Personal and Professional Development (I) | Mandatory Course | 1 | Self-Awareness, Goal Setting, Time Management, Stress Management, Communication Skills |
| 23GE101 | Engineering Graphics and Design | Core | 1 | Engineering Drawing, Orthographic Projections, Isometric Projections, Sectional Views, CAD Basics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23HS102 | Value Education | Core | 1 | Ethics, Morals, Human Values, Professional Ethics, Social Responsibility |
| 23MA102 | Engineering Mathematics II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Fourier Series, Partial Differential Equations, Complex Analysis |
| 23CY101 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion, Spectroscopy, Polymer Chemistry, Nanomaterials |
| 23CS201 | Data Structures | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Sorting & Searching |
| 23EC101 | Analog and Digital Electronics | Core | 3 | Diodes and Transistors, Amplifiers, Oscillators, Logic Gates, Combinational Circuits, Sequential Circuits |
| 23CS281 | Data Structures Laboratory | Lab | 1.5 | Implementation of Data Structures, Algorithm Analysis, Problem Solving, Recursion, Dynamic Programming |
| 23CY181 | Engineering Chemistry Laboratory | Lab | 1.5 | Titration, Spectrophotometry, pH Measurement, Material Analysis, Water Analysis |
| 23EC181 | Analog and Digital Electronics Laboratory | Lab | 1.5 | Circuit Design, Op-Amp Applications, Logic Gate Experiments, Flip-Flops, Counters, ADC/DAC |
| 23EN101 | Environmental Science and Engineering | Mandatory Course | 1 | Ecosystems, Biodiversity, Pollution Control, Waste Management, Sustainable Development |
| 23PD102 | Personal and Professional Development (II) | Mandatory Course | 1 | Goal Setting, Decision Making, Problem Solving, Interpersonal Skills, Teamwork |
| 23CS191 | Python Programming | Core | 1.5 | Python Basics, Data Structures, Functions, Modules, File I/O, Object-Oriented Programming |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA201 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables, Distributions, Hypothesis Testing, Regression Analysis |
| 23CS301 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Sorting Algorithms, Graph Algorithms, Dynamic Programming, Greedy Algorithms, NP-Completeness |
| 23CS302 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Deadlocks |
| 23CS303 | Database Management Systems | Core | 3 | Relational Model, SQL, ER Diagrams, Normalization, Transaction Management, Concurrency Control |
| 23CS304 | Object Oriented Programming | Core | 3 | OOP Concepts, Classes & Objects, Inheritance, Polymorphism, Abstraction, Exception Handling |
| 23CS381 | Operating Systems Laboratory | Lab | 1.5 | Linux Commands, Shell Scripting, Process Management, Thread Synchronization, Memory Allocation |
| 23CS382 | Database Management Systems Laboratory | Lab | 1.5 | SQL Queries, Database Design, PL/SQL, Triggers, Views, Stored Procedures |
| 23CS383 | Object Oriented Programming Laboratory | Lab | 1.5 | C++ or Java Programming, Class Design, Inheritance, Polymorphism, GUI Programming |
| 23PD201 | Personal and Professional Development (III) | Mandatory Course | 1 | Presentation Skills, Interview Skills, Resume Building, Group Discussion, Professional Etiquette |
| 23CS305 | Computer Architecture and Organization | Core | 3 | CPU Organization, Instruction Sets, Pipelining, Memory Hierarchy, I/O Organization, Control Unit |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS401 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| 23CS402 | Computer Networks | Core | 3 | Network Models, Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer, Network Security |
| 23CS403 | Artificial Intelligence | Specialization Core | 4 | AI Agents, Problem Solving, Search Algorithms, Knowledge Representation, Machine Learning Basics, Natural Language Processing |
| 23CS404 | Machine Learning | Specialization Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Neural Networks, Deep Learning Basics |
| 23CS481 | Computer Networks Laboratory | Lab | 1.5 | Network Configuration, Socket Programming, Protocol Implementation, Network Monitoring, Security Tools |
| 23CS482 | Artificial Intelligence Laboratory | Lab | 1.5 | Python for AI, Search Algorithms Implementation, Constraint Satisfaction Problems, Logic Programming, Expert Systems |
| 23CS483 | Machine Learning Laboratory | Lab | 1.5 | Data Preprocessing, Scikit-learn, Model Training & Evaluation, Regression, Classification, Clustering, Deep Learning Frameworks |
| 23PD202 | Personal and Professional Development (IV) | Mandatory Course | 1 | Emotional Intelligence, Conflict Resolution, Leadership Skills, Entrepreneurship Basics, Global Awareness |
| 23CS44X | Program Elective I | Elective | 3 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS501 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization, Symbol Table |
| 23CS502 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Design Principles, Testing, Project Management, Agile Methodologies |
| 23CS503 | Deep Learning | Specialization Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Models, Deep Learning Frameworks |
| 23CS504 | Natural Language Processing | Specialization Core | 4 | Text Preprocessing, Language Models, Word Embeddings, Sequence Models, Machine Translation, Sentiment Analysis |
| 23CS581 | Compiler Design Laboratory | Lab | 1.5 | Lexical Analyzer Implementation, Parser Implementation, Intermediate Code Generation, Compiler Tools (Lex, Yacc) |
| 23CS582 | Deep Learning Laboratory | Lab | 1.5 | TensorFlow/PyTorch, CNNs for Image Classification, RNNs for Sequence Prediction, Transformers Implementation, Model Deployment |
| 23CS583 | Natural Language Processing Laboratory | Lab | 1.5 | NLTK, SpaCy, Text Classification, Sentiment Analysis, Chatbot Development, Machine Translation Projects |
| 23PD301 | Personal and Professional Development (V) | Mandatory Course | 1 | Critical Thinking, Entrepreneurial Mindset, Ethical Hacking Basics, Cyber Security Awareness, Advanced Communication |
| 23CS54X | Program Elective II | Elective | 3 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS601 | Cryptography and Network Security | Core | 3 | Cryptographic Algorithms, Public Key Infrastructure, Network Security Protocols, Firewalls, Intrusion Detection Systems, Web Security |
| 23CS602 | Data Science and Big Data Analytics | Specialization Core | 4 | Data Collection, Data Preprocessing, Exploratory Data Analysis, Big Data Technologies (Hadoop, Spark), Predictive Modeling, Data Visualization |
| 23CS603 | Reinforcement Learning | Specialization Core | 4 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Q-Learning, Policy Gradient Methods |
| 23CS681 | Cryptography and Network Security Laboratory | Lab | 1.5 | Cryptographic Toolkits, Network Scanning, Vulnerability Assessment, Firewall Configuration, Intrusion Detection |
| 23CS682 | Data Science and Big Data Analytics Laboratory | Lab | 1.5 | Python for Data Science, R for Data Analytics, Hadoop Ecosystem, Spark Programming, Data Visualization Tools, Machine Learning Pipelines |
| 23CS683 | Reinforcement Learning Laboratory | Lab | 1.5 | OpenAI Gym, Q-Learning Implementation, Policy Gradient Algorithms, Deep Reinforcement Learning, Robotics Applications |
| 23CS691 | Internship / Industrial Training (4 Weeks) | Practical | 1 | Industry Exposure, Practical Skill Application, Project Work, Professional Networking, Report Writing |
| 23CS64X | Program Elective III | Elective | 3 | |
| 23OE60X | Open Elective I | Open Elective | 3 |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS701 | Distributed Systems | Core | 3 | Distributed System Architectures, Inter-process Communication, Distributed File Systems, Concurrency Control, Fault Tolerance, Cloud Computing Basics |
| 23CS702 | Robotics and Intelligent Systems | Specialization Core | 4 | Robot Kinematics, Sensors and Actuators, Robot Control, Path Planning, Machine Vision for Robotics, Human-Robot Interaction |
| 23CS791 | Project Work - Phase I | Project | 4 | Problem Identification, Literature Review, Project Design, Methodology, Feasibility Study, Proposal Writing |
| 23CS74X | Program Elective IV | Elective | 3 | |
| 23OE70X | Open Elective II | Open Elective | 3 | |
| 23HS701 | Professional Ethics and Human Values | Core | 2 | Ethical Theories, Engineering Ethics, Professional Responsibility, Cybersecurity Ethics, AI Ethics, Corporate Governance |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS801 | Cognitive Computing | Specialization Core | 4 | Cognitive Architectures, Knowledge Representation, Reasoning Systems, Natural Language Understanding, Computer Vision, Human-Computer Interaction |
| 23CS891 | Project Work - Phase II | Project | 8 | System Implementation, Testing & Debugging, Performance Evaluation, Documentation, Report Writing, Project Presentation |
| 23CS84X | Program Elective V | Elective | 3 | |
| 23OE80X | Open Elective III | Open Elective | 1 |
