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B-SC in Computer Science With Artificial Intelligence at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science with Artificial Intelligence at SRM Institute of Science and Technology Chengalpattu?
This B.Sc Computer Science with Artificial Intelligence program at SRM Institute of Science and Technology focuses on equipping students with core computing principles alongside advanced AI and Machine Learning capabilities. It''''s designed to meet the surging demand for AI professionals in India, blending theoretical knowledge with practical application in areas like deep learning, NLP, and big data analytics, thereby fostering innovation-ready graduates.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and problem-solving, seeking entry into the rapidly expanding AI sector. It also caters to individuals passionate about developing intelligent systems and solving complex real-world problems. Prerequisites include a foundational understanding of computer science concepts and analytical thinking.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as AI Engineers, Machine Learning Scientists, Data Analysts, and AI Consultants in leading tech companies and startups. Entry-level salaries typically range from INR 4-7 lakhs per annum, with significant growth trajectories. Professional certifications in AI/ML from platforms like Coursera or NVIDIA further enhance employability.
Student Success Practices
Foundation Stage
Master Programming & Logic- (Semester 1-2)
Dedicate time to thoroughly understand fundamental programming concepts in C and Python, along with digital logic. Practice coding daily on platforms like HackerRank or CodeChef to build strong problem-solving skills and debug efficiently.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Online C/Python IDEs
Career Connection
A solid foundation in programming and logic is crucial for all AI/ML roles, serving as the bedrock for advanced algorithm development and data manipulation needed for placements.
Build a Strong Mathematical Base- (Semester 1-2)
Focus on Professional Mathematics, particularly calculus, linear algebra, and probability, as these are the pillars of Machine Learning. Utilize resources like Khan Academy or NPTEL for deeper understanding and consistent practice.
Tools & Resources
Khan Academy, NPTEL, Mathematics textbooks, Wolfram Alpha
Career Connection
A robust mathematical understanding is indispensable for comprehending complex AI algorithms, enabling students to excel in advanced courses and interviews for AI research and development roles.
Engage in Early Project Exploration- (Semester 1-2)
Start working on small, self-initiated projects in AI/ML using basic Python. Explore simple datasets and implement foundational algorithms to gain practical experience beyond classroom assignments.
Tools & Resources
Kaggle datasets (for beginners), Jupyter Notebook, Google Colab, Scikit-learn
Career Connection
Early project work demonstrates proactive learning and practical application, making students stand out in internships and entry-level positions requiring hands-on experience in AI.
Intermediate Stage
Specialize through Electives and Certifications- (Semester 3-5)
Choose specialization electives strategically based on career interests (e.g., NLP, Computer Vision). Supplement coursework with industry-recognized certifications from platforms like Coursera, edX, or deeplearning.ai to gain specialized skills.
Tools & Resources
Coursera (AI/ML Specializations), edX, deeplearning.ai courses, NVIDIA DLI
Career Connection
Specialized skills and certifications validate expertise in niche AI domains, making students highly marketable for targeted roles in the Indian tech industry and increasing salary potential.
Participate in Hackathons & Competitions- (Semester 3-5)
Actively participate in AI/ML hackathons, data science competitions (e.g., Kaggle, Analytics Vidhya), and coding contests. This hones problem-solving under pressure and builds a portfolio of practical achievements.
Tools & Resources
Kaggle, Analytics Vidhya, Major League Hacking (MLH), College hackathons
Career Connection
Winning or even participating in such events demonstrates practical skill, teamwork, and innovation, which are highly valued by Indian employers for AI/ML roles and can lead to direct recruitment.
Seek Industry Internships- (Semester 4-5)
Actively apply for internships during summer breaks or semester breaks at startups, IT service companies, or R&D divisions in India. Leverage college career services and professional networking platforms like LinkedIn.
Tools & Resources
LinkedIn, Internshala, College placement cell, Company career pages
Career Connection
Internships provide invaluable industry exposure, professional networking opportunities, and often lead to pre-placement offers (PPOs), significantly boosting career prospects in the Indian job market.
Advanced Stage
Undertake a Comprehensive Major Project- (Semester 6)
Collaborate with faculty or industry mentors on a substantial AI/ML major project. Focus on a real-world problem, employing advanced techniques, and ensure robust documentation and presentation of results.
Tools & Resources
GitHub, Research papers (arXiv), Industry mentors, Project management tools
Career Connection
A well-executed major project serves as a capstone, showcasing deep technical skills and problem-solving abilities to potential employers, which is critical for securing high-value placements.
Build a Professional Portfolio and Network- (Semester 5-6)
Create an online portfolio (e.g., GitHub, personal website) showcasing projects, certifications, and skills. Attend industry seminars, workshops, and connect with professionals on LinkedIn to expand your network.
Tools & Resources
GitHub Pages, LinkedIn, Personal website builders, Conference/Webinar platforms
Career Connection
A strong professional portfolio and network are essential for career visibility, opening doors to advanced job opportunities, mentorship, and entrepreneurial ventures in the Indian AI ecosystem.
Master Interview & Aptitude Skills- (Semester 5-6)
Practice technical interviews focusing on core AI/ML concepts, data structures, algorithms, and logical reasoning. Engage in mock interviews and aptitude test preparation to enhance performance in placement drives.
Tools & Resources
LeetCode, GeeksforGeeks interview section, Mock interview platforms, Aptitude test books/websites
Career Connection
Excelling in technical interviews and aptitude tests is paramount for securing placements in top-tier Indian companies and MNCs, leading to desired roles and competitive salary packages.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or equivalent examination with Mathematics/Business Mathematics/Computer Science/Statistics as one of the subjects.
Duration: 6 semesters / 3 years
Credits: 140 Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEH101J | Communicative English | Core | 3 | Reading Comprehension, Writing Skills, Listening & Speaking, Grammar & Vocabulary, Presentation Skills |
| 21LEM101J | Professional Mathematics | Core | 4 | Calculus, Matrices, Differential Equations, Probability, Statistics |
| 21LCS101J | Programming in C | Core | 3 | C Fundamentals, Control Structures, Functions, Arrays & Pointers, Structures & Unions |
| 21LCS102J | Digital Logic & Computer Organization | Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Computer Architecture |
| 21LCS103J | Problem Solving using C Lab | Lab | 2 | C Programming Practice, Debugging, Conditional Statements, Looping Constructs, Function Implementation |
| 21LCS104J | Digital Logic Lab | Lab | 2 | Logic Gate Implementation, Combinational Circuit Design, Sequential Circuit Design, Computer Arithmetic |
| 21LCS105J | AI & ML for Problem Solving | Core | 3 | Introduction to AI/ML, Problem Solving Agents, Search Algorithms, Supervised Learning, Unsupervised Learning |
| 21LBS101J | Environmental Science | Ability Enhancement Compulsory Course | 2 | Ecosystems, Biodiversity, Pollution, Renewable Energy, Environmental Ethics |
| 21LVA101J | Value Added Course I | Value Added Course | 2 | Communication Skills, Teamwork, Critical Thinking, Problem-solving, Professional Etiquette |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEH201J | Principles of Management | Core | 3 | Management Functions, Planning & Organizing, Leading & Controlling, Motivation, Ethics in Management |
| 21LCS201J | Data Structures | Core | 4 | Arrays, Stacks & Queues, Linked Lists, Trees, Graphs, Sorting & Searching |
| 21LCS202J | Object Oriented Programming with Python | Core | 3 | Python Fundamentals, OOP Concepts, Classes & Objects, Inheritance, Polymorphism, Exception Handling |
| 21LCS203J | Operating Systems | Core | 3 | OS Concepts, Process Management, Memory Management, File Systems, I/O Systems |
| 21LCS204J | Data Structures Lab | Lab | 2 | Array Operations, Stack/Queue Implementation, Linked List Operations, Tree/Graph Traversal, Sorting/Searching Algorithms |
| 21LCS205J | Object Oriented Programming with Python Lab | Lab | 2 | Python Programming Practice, OOP Implementation, GUI Programming, File I/O, Database Connectivity |
| 21LCS206J | Database Management Systems | Core | 3 | DBMS Concepts, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| 21LCS207J | Database Management Systems Lab | Lab | 2 | SQL Queries, Database Design, Stored Procedures, Triggers, Data Manipulation |
| 21LBS201J | Indian Constitution | Ability Enhancement Compulsory Course | 2 | Preamble, Fundamental Rights, Directive Principles, Union & State Governments, Constitutional Amendments |
| 21LVA201J | Value Added Course II | Value Added Course | 2 | Ethics and Values, Self-Management, Digital Literacy, Creative Thinking, Interpersonal Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS301J | Computer Networks | Core | 3 | Network Models, Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| 21LCS302J | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Feature Engineering, Deep Learning Introduction |
| 21LCS303J | Data Analytics | Core | 3 | Data Collection, Data Cleaning, Data Visualization, Exploratory Data Analysis, Statistical Analysis, Predictive Modeling |
| 21LCS304J | Web Technology | Core | 3 | HTML, CSS, JavaScript, Web Servers, Client-Server Architecture, Web Services |
| 21LCS305J | Computer Networks Lab | Lab | 2 | Network Device Configuration, Socket Programming, Protocol Analysis, Network Security Tools |
| 21LCS306J | Machine Learning Lab | Lab | 2 | ML Algorithm Implementation, Model Training, Evaluation Metrics, Data Preprocessing, Scikit-learn, TensorFlow/PyTorch Basics |
| 21LCS307J | Data Analytics Lab | Lab | 2 | Data Cleaning Tools, Visualization Tools, Statistical Software, Regression Analysis, Classification |
| 21LCS308J | Web Technology Lab | Lab | 2 | HTML/CSS Design, JavaScript Interactivity, Dynamic Web Pages, Frontend Frameworks |
| 21LSS301J | Skill Enhancement Course I | Skill Enhancement Course | 2 | Advanced Excel Skills, Public Speaking, Entrepreneurship Basics, Leadership Development, Foreign Language Basics |
| 21LVA301J | Value Added Course III | Value Added Course | 2 | Financial Literacy, Stress Management, Time Management, Interview Skills, Professional Networking |
| 21LGE3XXJ | Generic Elective I | Elective | 3 | Introduction to Business, Principles of Economics, Sociology, Psychology Basics, Art & Culture |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS401J | Artificial Intelligence | Core | 4 | AI History, Search Algorithms, Knowledge Representation, Logic Programming, Planning, Expert Systems |
| 21LCS402J | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Design Principles, Testing, Maintenance, Project Management |
| 21LCS403J | Cloud Computing | Core | 3 | Cloud Paradigms, Service Models, Deployment Models, Virtualization, Cloud Security, AWS/Azure Basics |
| 21LCS404J | Natural Language Processing | Core | 3 | Text Preprocessing, NLP Tasks, Language Models, Machine Translation, Sentiment Analysis, Text Classification |
| 21LCS405J | Artificial Intelligence Lab | Lab | 2 | Search Algorithm Implementation, Logic Programming, AI Agent Design, Game Playing, Expert System Development |
| 21LCS406J | Cloud Computing Lab | Lab | 2 | Cloud Service Deployment, Virtual Machine Management, Storage Services, Serverless Computing, Containerization |
| 21LCS407J | Natural Language Processing Lab | Lab | 2 | Text Processing Libraries, NLP Model Training, Chatbot Development, Information Extraction |
| 21LCS408J | Internship/Industrial Training | Project | 1 | Industry Exposure, Project Implementation, Professional Skills, Report Writing |
| 21LSS401J | Skill Enhancement Course II | Skill Enhancement Course | 2 | Data Visualization Tools, Project Management Software, Advanced Communication, Negotiation Skills, Intellectual Property Rights |
| 21LVA401J | Value Added Course IV | Value Added Course | 2 | Cyber Security Awareness, Web Development Basics, Mobile App Development Basics, Basic Data Science Tools, Career Planning |
| 21LGE4XXJ | Generic Elective II | Elective | 3 | Environmental Studies, Indian History, Political Science, Entrepreneurship, Introduction to Law |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS501J | Deep Learning | Core | 4 | Neural Networks, CNNs, RNNs, LSTMs, Autoencoders, Generative Models, Deep Learning Frameworks |
| 21LCS502J | Research Methodology | Core | 3 | Research Problem Formulation, Literature Review, Research Design, Data Collection, Statistical Analysis, Report Writing |
| 21LCS503J | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop Ecosystem, Spark, NoSQL Databases, Data Warehousing, Data Streaming |
| 21LCS504J | Data Mining | Core | 3 | Data Preprocessing, Classification, Clustering, Association Rule Mining, Anomaly Detection, Web Mining |
| 21LCS505J | Deep Learning Lab | Lab | 2 | Neural Network Implementation, Image Classification, Sequence Prediction, Generative Adversarial Networks |
| 21LCS506J | Big Data Analytics Lab | Lab | 2 | Hadoop/Spark Cluster Setup, Data Processing, MapReduce Programming, Data Ingestion, Real-time Analytics |
| 21LCS507J | Data Mining Lab | Lab | 2 | Data Cleaning, Classification Algorithms, Clustering Algorithms, Association Rule Discovery |
| 21LVA501J | Value Added Course V | Value Added Course | 2 | IoT Fundamentals, Robotics Basics, Blockchain Technology, Green Computing, Professional Certifications |
| 21LGE5XXJ | Generic Elective III | Elective | 3 | Disaster Management, Human Rights, Indian Traditional Knowledge, Sports Science, Foreign Language |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS601J | Ethics in AI | Core | 3 | AI Ethics, Bias in AI, Privacy Concerns, Accountability, Responsible AI Development, Legal Implications |
| 21LCS602J | Reinforcement Learning | Core | 4 | Markov Decision Processes, Value Iteration, Policy Iteration, Q-Learning, Deep Reinforcement Learning, Applications |
| 21LCS603J | Specialization Elective I | Elective | 3 | Image Processing, Computer Vision, Robotics, Internet of Things (IoT), Cybersecurity Fundamentals |
| 21LCS604J | Specialization Elective II | Elective | 3 | Advanced Data Structures, Game Development, Bioinformatics, Quantum Computing Basics, DevOps |
| 21LCS605J | Reinforcement Learning Lab | Lab | 2 | Reinforcement Learning Agents, Environment Simulation, Policy Optimization, Deep Q-Networks |
| 21LCS606J | Major Project | Project | 6 | Problem Identification, Solution Design, Implementation, Testing, Evaluation, Report Writing, Presentation |
