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B-SC 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 and Machine Learning program at SRM Institute of Science and Technology focuses on equipping students with core competencies in AI, machine learning, deep learning, and data analytics. It addresses the burgeoning demand for skilled AI professionals in the Indian market, covering theoretical foundations and practical applications. The program aims to foster innovation and problem-solving abilities crucial for modern technological challenges.
Who Should Apply?
This program is ideal for fresh graduates with a strong mathematical and logical aptitude seeking entry into the AI and data science fields. It also suits working professionals looking to upskill in cutting-edge AI technologies or career changers transitioning into the rapidly expanding AI industry in India. Prerequisites typically include a 10+2 background with Mathematics and Computer Science.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including AI Engineer, Machine Learning Specialist, Data Scientist, NLP Engineer, and Computer Vision Engineer. Entry-level salaries range from INR 4-7 LPA, growing significantly with experience. The program aligns with industry demands, preparing students for roles in startups, IT giants, and research institutions across the country.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Logic- (Semester 1-2)
Dedicate significant time to mastering C/C++ and Python programming, along with discrete mathematics. Regularly solve problems on platforms like HackerRank, CodeChef, and GeeksforGeeks to build strong algorithmic thinking. Form study groups to discuss complex problems and collaborate on basic projects.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Online tutorials (e.g., Python.org, Cppreference.com)
Career Connection
A strong foundation in programming and logic is critical for clearing technical interviews and excelling in core AI coursework, leading to better internship and placement prospects.
Build a Foundational Project Portfolio- (Semester 1-2)
Start building small, hands-on projects related to core programming concepts, data structures, and object-oriented programming. Even simple projects like a calculator, a to-do list application, or basic data analysis scripts can showcase early skills.
Tools & Resources
GitHub, Jupyter Notebooks, VS Code
Career Connection
Early projects demonstrate practical application of learned concepts, making resumes more appealing to recruiters and providing talking points for interviews.
Engage in Technical Clubs and Workshops- (Semester 1-2)
Join university computer science or AI clubs to network with peers and seniors. Participate in beginner-friendly workshops on new technologies, coding competitions, or hackathons to gain exposure and practical experience beyond the curriculum.
Tools & Resources
SRMIST Tech Clubs, Workshop announcements, Internal university events
Career Connection
Active participation fosters peer learning, exposes you to industry trends, and helps develop soft skills vital for professional success and career growth.
Intermediate Stage
Deep Dive into AI/ML Libraries and Frameworks- (Semester 3-5)
Beyond theoretical understanding of AI and ML, gain proficiency in practical tools. Master libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and Keras. Work through online courses and tutorials to implement various ML algorithms.
Tools & Resources
Coursera/edX (e.g., Andrew Ng''''s ML course), Kaggle tutorials, Official documentation (TensorFlow, PyTorch)
Career Connection
Hands-on experience with industry-standard tools is a prerequisite for most AI/ML roles and crucial for building robust models in projects and internships.
Participate in AI/ML Competitions and Hackathons- (Semester 3-5)
Actively engage in platforms like Kaggle for data science competitions or university/national level AI hackathons. This provides real-world problem-solving experience, builds a competitive portfolio, and sharpens analytical skills under pressure.
Tools & Resources
Kaggle, Devfolio, Major League Hacking (MLH) events
Career Connection
Winning or even participating in such events adds significant value to your resume, demonstrates initiative, and helps in networking with industry professionals and potential employers.
Seek Industry Internships and Live Projects- (Semester 3-5)
Apply for internships at startups or established companies focusing on AI/ML. Even short-term or unpaid internships offer invaluable industry exposure, mentorship, and a chance to apply academic knowledge to real business problems. Leverage university career services.
Tools & Resources
Internshala, LinkedIn Jobs, University Career Services Portal
Career Connection
Internships are often a direct gateway to full-time employment, providing practical experience, building professional networks, and making you job-ready before graduation.
Advanced Stage
Specialize and Build an Advanced Portfolio- (Semester 6)
Choose a specific area within AI (e.g., NLP, Computer Vision, Reinforcement Learning) and develop a specialized portfolio. This could include a major project, research paper, or contribution to open-source AI projects. Showcase complex models and solutions.
Tools & Resources
GitHub, arXiv.org (for research papers), Personal website/blog
Career Connection
Specialized projects highlight expertise, making you a strong candidate for advanced roles in specific AI domains and potentially leading to research opportunities or niche tech companies.
Intensive Placement and Interview Preparation- (Semester 6)
Focus on rigorous preparation for technical interviews, including data structures and algorithms, machine learning concepts, and system design. Practice mock interviews, solve case studies, and refine soft skills for HR rounds. Attend campus placement workshops.
Tools & Resources
LeetCode, GeeksforGeeks Interview Prep, Glassdoor for company-specific questions, University Placement Cell
Career Connection
Thorough preparation directly translates into higher success rates in securing placements with top-tier companies offering competitive salaries and growth opportunities.
Network and Stay Updated with Industry Trends- (Semester 6)
Attend industry conferences, webinars, and tech meetups (online and offline). Connect with professionals on LinkedIn, follow leading AI researchers and companies. Continuously read blogs, research papers, and news to stay abreast of the latest advancements in AI.
Tools & Resources
LinkedIn, Medium (AI blogs), arXiv, TechCrunch/The Verge AI sections
Career Connection
Networking opens doors to hidden opportunities, mentorship, and keeps your skills relevant, ensuring long-term career growth and adaptability in the dynamic AI industry.
Program Structure and Curriculum
Eligibility:
- 10+2 with a minimum aggregate of 50% in Physics, Chemistry, and Mathematics/Computer Science/Biology from a recognized board.
Duration: 3 years / 6 semesters
Credits: 131 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UCU1501 | English I | Core | 3 | Grammar and Usage, Vocabulary Building, Reading Comprehension, Basic Writing Skills, Communication Strategies |
| UMA1501 | Mathematics for Computer Science I | Core | 4 | Set Theory and Logic, Relations and Functions, Matrix Algebra, Combinatorics, Graph Theory |
| UCS1501 | Programming in C | Core | 3 | C Language Fundamentals, Control Structures, Functions and Arrays, Pointers, Structures and File I/O |
| UCS1502 | Digital Logic Fundamentals | Core | 3 | Number Systems, Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits |
| UCS1503 | Computer Fundamentals | Core | 3 | Computer Generations, Hardware Components, Software Concepts, Operating System Basics, Networking Fundamentals |
| UCS1511 | Programming in C Lab | Lab | 2 | Conditional Statements Practice, Looping Constructs Implementation, Function and Array Exercises, Pointer Applications, File Operations |
| UCS1512 | Digital Logic Fundamentals Lab | Lab | 2 | Logic Gates Implementation, Boolean Function Realization, Adders and Subtractors, Flip-Flops Design, Counters and Registers |
| UCW1501 | Value Education | Ability Enhancement | 1 | Human Values, Professional Ethics, Moral Development, Social Responsibility, Personal Growth |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UCU1502 | English II | Core | 3 | Advanced Grammar, Formal and Informal Communication, Report Writing, Technical Writing Basics, Presentation Skills |
| UMA1502 | Mathematics for Computer Science II | Core | 4 | Differential Equations, Integral Calculus, Vector Calculus, Numerical Methods, Fourier Series |
| UCS1504 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| UCS1505 | Object Oriented Programming with C++ | Core | 3 | Classes and Objects, Inheritance, Polymorphism, Abstraction and Encapsulation, Operator Overloading |
| UCS1506 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| UCS1513 | Data Structures Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| UCS1514 | Object Oriented Programming with C++ Lab | Lab | 2 | Class and Object Creation, Inheritance Implementation, Polymorphism Examples, Friend Functions, Exception Handling |
| UCW1502 | Environmental Studies | Ability Enhancement | 1 | Ecosystems, Biodiversity, Pollution Control, Natural Resources, Sustainable Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UMA1503 | Probability and Statistics | Core | 4 | Basic Probability Theory, Random Variables, Probability Distributions, Sampling Theory, Hypothesis Testing |
| UCS1507 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, Database Design, Normalization, Transaction Management |
| UCS1508 | Java Programming | Core | 3 | Java Fundamentals, OOP in Java, Exception Handling, Multithreading, Collections Framework |
| UCA1501 | Introduction to Artificial Intelligence | Core-AI | 4 | History and Foundations of AI, Intelligent Agents, Problem-Solving through Search, Knowledge Representation, Expert Systems |
| UCA1502 | Python Programming for AI | Core-AI | 3 | Python Basics, Data Structures in Python, Functions and Modules, NumPy and Pandas, File Handling |
| UCS1515 | Database Management Systems Lab | Lab | 2 | SQL DDL and DML Commands, Join Operations, Views and Stored Procedures, Database Connectivity, Normalization Practice |
| UCA1511 | Python Programming for AI Lab | Lab | 2 | Python Scripting for Data, NumPy Array Manipulations, Pandas Dataframe Operations, Data Visualization with Matplotlib, Basic ML Library Usage |
| UCS15E1 | Elective I | Discipline Specific Elective | 3 | Selected topics relevant to AI/CS discipline |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UCS1509 | Computer Networks | Core | 3 | OSI and TCP/IP Models, Network Topologies, IP Addressing and Subnetting, Routing Protocols, Transport Layer Services |
| UCA1503 | Machine Learning Fundamentals | Core-AI | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Clustering Techniques |
| UCA1504 | Data Preprocessing and Analysis | Core-AI | 3 | Data Cleaning, Data Integration, Feature Engineering, Exploratory Data Analysis, Statistical Analysis |
| UCA1505 | Natural Language Processing | Core-AI | 4 | Text Preprocessing, Tokenization and Stemming, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis |
| UCS1516 | Computer Networks Lab | Lab | 2 | Socket Programming, Network Configuration, Packet Tracing Tools, Client-Server Communication, Routing Protocols Simulation |
| UCA1512 | Machine Learning Lab | Lab | 2 | Implementing Regression Models, Classification Algorithms Practice, Clustering with Scikit-learn, Model Evaluation Metrics, Hyperparameter Tuning |
| UCS15E2 | Elective II | Discipline Specific Elective | 3 | Selected topics relevant to AI/CS discipline |
| UCW1503 | Soft Skills & Aptitude | Skill Enhancement | 1 | Communication Skills, Logical Reasoning, Quantitative Aptitude, Teamwork and Leadership, Interview Preparation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UCA1506 | Deep Learning | Core-AI | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTM and GRU Networks, Transformers |
| UCA1507 | Computer Vision | Core-AI | 4 | Image Processing Basics, Feature Detection, Object Recognition, Image Segmentation, Deep Learning for Vision |
| UCA1508 | Reinforcement Learning | Core-AI | 4 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-Learning, Deep Reinforcement Learning |
| UCS15P1 | Minor Project | Project | 4 | Problem Identification, Literature Survey, System Design, Implementation and Testing, Report Writing and Presentation |
| UCA1513 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras Basics, Building CNNs for Image Classification, RNNs for Sequence Data, Transfer Learning, Generative Adversarial Networks (GANs) |
| UCS15OE1 | Open Elective I | Open Elective | 3 | Cross-disciplinary subjects as chosen by student |
| UCS15E3 | Elective III | Discipline Specific Elective | 3 | Selected topics relevant to AI/CS discipline |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UCA1509 | AI Ethics and Governance | Core-AI | 3 | Ethical Principles in AI, Bias and Fairness in AI, AI Accountability, Privacy and Data Security, AI Regulations and Policies |
| UCA1510 | Big Data Analytics for AI | Core-AI | 4 | Big Data Concepts, Hadoop Ecosystem, Spark Framework, Distributed Storage, Real-time Data Processing |
| UCS15P2 | Major Project | Project | 6 | Advanced Problem Formulation, Solution Design and Architecture, Extensive Implementation, Testing and Validation, Project Defense and Documentation |
| UCS15E4 | Elective IV | Discipline Specific Elective | 3 | Selected topics relevant to AI/CS discipline |
| UCS15OE2 | Open Elective II | Open Elective | 3 | Cross-disciplinary subjects as chosen by student |
