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B-TECH in Computer Science And Engineering Artificial Intelligence And Machine Learning at Vellore Institute of Technology
Vellore, Tamil Nadu
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About the Specialization
What is Computer Science and Engineering (Artificial Intelligence and Machine Learning) at Vellore Institute of Technology Vellore?
This Computer Science and Engineering (Artificial Intelligence and Machine Learning) program at Vellore Institute of Technology focuses on equipping students with advanced skills in AI, ML, and Deep Learning, crucial for solving complex real-world problems. The curriculum emphasizes both theoretical foundations and practical applications, preparing graduates for the rapidly evolving Indian tech landscape. It distinguishes itself by integrating core CSE principles with specialized AI/ML methodologies, catering to the growing demand for intelligent systems.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude in mathematics and programming, seeking entry into high-growth AI/ML roles. It also suits working professionals aiming to upskill in cutting-edge technologies, and career changers transitioning into the AI industry. Candidates with a science or engineering background and a keen interest in data-driven innovation and intelligent systems will find this specialization highly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths as AI Engineers, Machine Learning Scientists, Data Scientists, or Robotics Engineers in India''''s booming tech sector. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly higher. The program fosters critical thinking, problem-solving, and innovation, aligning with certifications like AWS ML Specialty or Google Professional Machine Learning Engineer, ensuring robust growth trajectories in leading Indian companies and MNCs.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate significant effort to building a strong foundation in Python and C/C++. Practice coding regularly through online platforms and participate in basic coding contests. Understand data structures and algorithms thoroughly, as they are the bedrock for advanced AI/ML concepts.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Jupyter Notebooks
Career Connection
Strong coding skills are non-negotiable for AI/ML roles and are heavily tested in technical interviews. Early mastery accelerates learning in advanced courses and leads to better internship opportunities.
Build a Solid Mathematical Core- (Semester 1-3)
Focus on understanding Linear Algebra, Calculus, Probability, and Statistics. These mathematical concepts underpin all Machine Learning and Deep Learning algorithms. Supplement classroom learning with online courses and practice problems to solidify comprehension.
Tools & Resources
Khan Academy, MIT OpenCourseware (Linear Algebra, Probability), 3Blue1Brown YouTube Channel
Career Connection
A deep mathematical understanding enables you to debug models, understand research papers, and develop novel algorithms, setting you apart in advanced AI/ML research and development roles.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups, discuss challenging concepts, and collaborate on small projects with peers. Teaching others solidifies your own understanding, and working in teams simulates real-world development environments. Participate in university-level hackathons.
Tools & Resources
Discord, GitHub, Google Meet, VIT Tech Clubs
Career Connection
Develops crucial teamwork, communication, and problem-solving skills highly valued by employers, while also expanding your professional network.
Intermediate Stage
Undertake Practical AI/ML Projects and Internships- (Semester 3-5)
Apply theoretical knowledge by working on mini-projects using libraries like Scikit-learn, TensorFlow, or PyTorch. Seek out short-term internships or research assistantships to gain exposure to real-world AI/ML challenges and industry practices.
Tools & Resources
Kaggle, GitHub, TensorFlow, PyTorch, LinkedIn for internships
Career Connection
Practical experience and a strong project portfolio are essential for demonstrating your skills to recruiters. Internships often lead to pre-placement offers (PPOs) at leading tech companies in India.
Specialize in a Niche and Build a Portfolio- (Semester 4-6)
Identify an area within AI/ML (e.g., NLP, Computer Vision, Reinforcement Learning) that interests you most and delve deeper. Take relevant electives, complete online specializations, and create a portfolio of projects showcasing your expertise in that niche.
Tools & Resources
Coursera (Andrew Ng''''s courses), Udemy, Medium (for technical blogs), Personal website/GitHub
Career Connection
Specialization makes you a more attractive candidate for targeted roles. A dedicated portfolio visually demonstrates your capabilities and passion for specific AI/ML domains, enhancing job prospects.
Participate in AI/ML Competitions and Workshops- (Semester 3-6)
Actively participate in national and international AI/ML hackathons and challenges on platforms like Kaggle, DrivenData, or university-organized events. Attend workshops and seminars to stay updated with the latest trends and network with experts.
Tools & Resources
Kaggle, Analytics Vidhya, Meetup groups for AI/ML events, VIT Departmental Workshops
Career Connection
Competitions provide hands-on experience with diverse datasets and real-world problems, improve problem-solving under pressure, and offer opportunities for recognition, which can boost your resume and networking for placements.
Advanced Stage
Focus on Industry-Relevant Capstone Projects- (Semester 6-8)
For your final year project, choose a problem with real-world applicability, ideally in collaboration with an industry partner or a research lab. Aim for measurable outcomes and potential for deployment. Thoroughly document your process and results.
Tools & Resources
VIT Placement Cell for industry contacts, Research papers (e.g., arXiv, Google Scholar), Jira for project management
Career Connection
A high-impact capstone project acts as a compelling demonstration of your ability to tackle complex AI/ML problems, significantly enhancing your resume for placements in core AI companies.
Intensive Placement Preparation and Mock Interviews- (Semester 7-8)
Start preparing for placements well in advance. Practice coding interviews, brush up on theoretical concepts of AI/ML, data structures, and algorithms. Participate in mock interviews with peers, seniors, and career services to refine communication and technical skills.
Tools & Resources
LeetCode, Interviewer.ai, VIT Career Development Centre, Glassdoor
Career Connection
Targeted preparation is critical for securing top placements. Mastering interview skills and technical rounds is paramount for landing roles in leading Indian and global tech firms.
Network Strategically and Build Professional Presence- (Semester 6-8)
Attend industry conferences, connect with alumni and professionals on LinkedIn, and contribute to open-source projects. Cultivate a professional online presence. Networking opens doors to mentorship, internships, and job opportunities beyond the campus placement drive.
Tools & Resources
LinkedIn, GitHub, IEEE/ACM student chapters, Industry conferences (e.g., India AI Conclave)
Career Connection
Strong professional networks provide insights into industry trends, potential job referrals, and mentorship opportunities that can accelerate your career growth in the competitive Indian AI job market.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics/Biology with a minimum aggregate of 55% for General category. Mandatory appearance in VITEEE.
Duration: 4 years / 8 semesters
Credits: 183 Credits
Assessment: Internal: 60% (Continuous Assessment Tests, Digital Assignments, Quizzes, Projects/Labs), External: 40% (Final Assessment Test)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PHY1701 | Engineering Physics | Core | 3 | Quantum Physics, Laser Technology, Fiber Optics, Non-Destructive Testing, Material Science |
| PHY1901 | Engineering Physics Lab | Lab | 1 | Laser Diffraction, Optical Fiber Communication, Ultrasonic Interferometer, Hall Effect, Four Probe Method |
| CHY1701 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry, Corrosion and its Control, Fuels and Combustion, Polymer Chemistry |
| CHY1901 | Engineering Chemistry Lab | Lab | 1 | Volumetric Titrations, Conductometric Titrations, Potentiometric Titrations, pH Metry, Colorimetry |
| MAT1011 | Calculus for Engineers | Core | 4 | Differential Calculus, Functions of Several Variables, Integral Calculus, Multiple Integrals, Vector Calculus |
| CSE1001 | Problem Solving and Programming | Core | 3 | Problem Solving Techniques, Python Programming Fundamentals, Data Structures, Functions and Modules, File Handling |
| CSE1002 | Problem Solving and Programming Lab | Lab | 2 | Basic Python Programming, Control Flow, Functions, Lists and Tuples, Dictionaries and Sets |
| ENG1001 | Foundational English | Core | 2 | Reading Comprehension, Grammar and Usage, Writing Skills, Listening and Speaking, Vocabulary Building |
| EVS1001 | Environmental Sciences | Core | 1 | Ecology and Ecosystems, Biodiversity, Environmental Pollution, Waste Management, Sustainable Development |
| STS1001 | Soft Skills | Soft Skills | 1 | Self-Introduction, Goal Setting, Time Management, Presentation Skills, Group Discussion |
| FCS1001 | Foreign Language/Soft Skills | Core | 2 | Basic greetings in foreign language, Elementary grammar, Cultural nuances, Communication basics, Interpersonal skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT2001 | Linear Algebra | Core | 4 | Matrices and Determinants, Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Orthogonality |
| CSE2001 | Data Structures and Algorithms | Core | 3 | Abstract Data Types, Linear Data Structures, Non-Linear Data Structures, Searching and Sorting, Graph Algorithms |
| CSE2002 | Data Structures and Algorithms Lab | Lab | 2 | Array and Linked List Operations, Stack and Queue Implementations, Tree Traversal Algorithms, Graph Algorithms Implementation, Sorting and Searching Algorithms |
| ECE1001 | Basic Electrical and Electronics Engineering | Core | 3 | DC and AC Circuits, Semiconductor Devices, Transistors, Operational Amplifiers, Digital Logic |
| ECE1002 | Basic Electrical and Electronics Engineering Lab | Lab | 1 | Ohm''''s Law and Kirchhoff''''s Laws, PN Junction Diode Characteristics, Transistor biasing, Rectifier Circuits, Logic Gates |
| CSE1003 | Object-Oriented Programming | Core | 3 | Classes and Objects, Encapsulation and Abstraction, Inheritance, Polymorphism, Exception Handling |
| CSE1004 | Object-Oriented Programming Lab | Lab | 2 | Class and Object Creation, Constructor Overloading, Inheritance Implementation, Polymorphism Concepts, File I/O and Exception Handling |
| STS1002 | Soft Skills | Soft Skills | 1 | Critical Thinking, Problem Solving, Analytical Skills, Decision Making, Lateral Thinking |
| CSE2003 | Digital Logic and Computer Architecture | Core | 3 | Boolean Algebra, Combinational Circuits, Sequential Circuits, Processor Design, Memory Hierarchy |
| CSE2004 | Digital Logic and Computer Architecture Lab | Lab | 2 | Logic Gate Implementation, Multiplexers and Demultiplexers, Flip-flops and Registers, ALU Design, Memory Interfacing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT3001 | Discrete Mathematics | Core | 4 | Logic and Proofs, Set Theory, Combinatorics, Graph Theory, Algebraic Structures |
| CSE3001 | Operating Systems | Core | 3 | Operating System Structures, Process Management, CPU Scheduling, Memory Management, File Systems |
| CSE3002 | Operating Systems Lab | Lab | 2 | Shell Programming, Process Creation, CPU Scheduling Algorithms, Memory Allocation Schemes, Deadlock Detection |
| CSE3003 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, Database Design, Normalization, Transaction Management |
| CSE3004 | Database Management Systems Lab | Lab | 2 | SQL DDL and DML, Joins and Subqueries, Views and Stored Procedures, Triggers, Database Connectivity |
| CSE3005 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Decidability and Undecidability |
| CSE3006 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport Layer |
| CSE3007 | Computer Networks Lab | Lab | 2 | Network Configuration, Socket Programming, Routing Protocols, Network Security Tools, Packet Tracing |
| AIML3001 | Foundations of AI and ML | Core | 3 | Introduction to AI, Problem Solving Agents, Knowledge Representation, Introduction to Machine Learning, Supervised Learning Basics |
| STS2001 | Soft Skills | Soft Skills | 1 | Verbal Aptitude, Quantitative Aptitude, Logical Reasoning, Critical Reading, Data Interpretation |
| ENG1002 | Professional English | Core | 2 | Professional Communication, Report Writing, Presentation Skills, Technical Writing, Intercultural Communication |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT3002 | Probability and Statistics | Core | 4 | Probability Axioms, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing |
| CSE4001 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| CSE4002 | Compiler Design Lab | Lab | 2 | Lexical Analyzer using LEX, Parser using YACC, Syntax Directed Translation, Symbol Table Management, Code Generation |
| AIML4001 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Ensemble Methods |
| AIML4002 | Machine Learning Lab | Lab | 2 | Linear Regression, Logistic Regression, Decision Trees, Clustering Algorithms, Support Vector Machines |
| AIML4003 | Deep Learning | Core | 3 | Neural Networks, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, Deep Learning Frameworks |
| AIML4004 | Deep Learning Lab | Lab | 2 | Perceptrons, Multi-layer Perceptrons, CNN Implementation, RNN Implementation, Transfer Learning |
| STS2002 | Soft Skills | Soft Skills | 1 | Communication Barriers, Conflict Resolution, Team Building, Emotional Intelligence, Interpersonal Skills |
| ITE3001 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Software Maintenance |
| AIML4005 | Data Visualization | Core | 3 | Introduction to Data Visualization, Data Types and Visualizations, Visualization Techniques, Interactive Visualization, Visualization Tools |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIML5001 | Natural Language Processing | Core | 3 | Text Preprocessing, Language Modeling, Syntactic Analysis, Semantic Analysis, Information Extraction |
| AIML5002 | Natural Language Processing Lab | Lab | 2 | Tokenization and Stemming, POS Tagging, Named Entity Recognition, Text Classification, Sentiment Analysis |
| AIML5003 | Computer Vision | Core | 3 | Image Formation, Image Processing, Feature Detection, Object Recognition, Motion Analysis |
| AIML5004 | Computer Vision Lab | Lab | 2 | Image Filtering, Edge Detection, Corner Detection, Object Tracking, Image Segmentation |
| AIML5005 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Deep Reinforcement Learning |
| AIML5006 | Reinforcement Learning Lab | Lab | 2 | Value Iteration, Policy Iteration, Q-Learning, SARSA, Gym Environments |
| STS3001 | Soft Skills | Soft Skills | 1 | Resume Writing, Cover Letter, Interview Skills, Mock Interviews, Personal Grooming |
| VCPJ5001 | Capstone Project - I (Phase I) | Project | 6 | Problem Identification, Literature Survey, Requirement Analysis, System Design, Project Proposal |
| CSE3501 | Program Elective: Network Security and Cryptography | Elective | 3 | Symmetric Key Cryptography, Asymmetric Key Cryptography, Hash Functions, Digital Signatures, Network Security Protocols |
| CSE3502 | Program Elective: Cloud Computing Fundamentals | Elective | 3 | Cloud Deployment Models, Cloud Service Models, Virtualization, Cloud Security, Cloud Platforms |
| CSE3503 | Program Elective: Human Computer Interaction | Elective | 3 | Interaction Design Principles, User Interface Design, Usability Testing, User Experience (UX), Cognitive Psychology in HCI |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIML6001 | Big Data Analytics | Core | 3 | Big Data Technologies, Hadoop Ecosystem, Spark Framework, NoSQL Databases, Data Stream Processing |
| AIML6002 | Big Data Analytics Lab | Lab | 2 | HDFS Operations, MapReduce Programming, Spark RDDs and DataFrames, Hive Queries, Kafka Streams |
| STS3002 | Soft Skills | Soft Skills | 1 | Group Discussion Etiquette, Negotiation Skills, Teamwork, Leadership Styles, Conflict Management |
| VCPJ6001 | Capstone Project - II (Phase II) | Project | 6 | Implementation Phase, Testing and Debugging, Documentation, Interim Report, Presentation Skills |
| AIMLPE A | Programme Elective for AIML | Elective | 3 | To be chosen from a pool of specialized subjects., Examples include: Medical Image Computing, Explainable AI, Speech Recognition. |
| AIMLPE B | Programme Elective for AIML | Elective | 3 | To be chosen from a pool of specialized subjects., Examples include: Robotics and Automation, Time Series Analysis, AI for Cybersecurity. |
| UE A | University Elective | Elective | 3 | To be chosen from a university-wide pool of general subjects., Examples include: Entrepreneurship, Foreign Languages, Digital Marketing. |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIML7001 | Ethical AI and Trustworthy ML | Core | 3 | AI Ethics Principles, Bias in AI, Fairness and Transparency, Privacy in AI Systems, Accountability and Governance |
| VCPJ7001 | Capstone Project - III (Phase III) | Project | 6 | Final Implementation, Evaluation and Benchmarking, Report Writing, Final Presentation, Deployment Strategies |
| AIMLPE C | Programme Elective for AIML | Elective | 3 | To be chosen from a pool of specialized subjects., Examples include: Explainable AI, Speech Recognition, Recommendation Systems. |
| UE B | University Elective | Elective | 3 | To be chosen from a university-wide pool of general subjects., Examples include: Introduction to Data Science, Financial Management, Public Speaking. |
| VLCJ7001 | Industrial Internship | Core | 6 | Industry problem solving, Practical application of skills, Professional communication, Team collaboration, Project report preparation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VCPR8001 | Capstone Project - IV (Phase IV) | Project | 15 | Project Deployment, Performance Optimization, Comprehensive Documentation, Final Viva Voce, Research Publication (if applicable) |
