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B-TECH in Artificial Intelligence at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Thiruvallur, Tamil Nadu
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About the Specialization
What is Artificial Intelligence at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvallur?
This Artificial Intelligence (AI) program at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology focuses on equipping students with deep knowledge in machine learning, deep learning, natural language processing, and robotics. With India''''s rapid digital transformation, AI is crucial for innovation in sectors like healthcare, finance, and automotive. The program emphasizes hands-on experience and addresses the growing demand for skilled AI professionals in the Indian market.
Who Should Apply?
This program is ideal for aspiring engineers with a strong aptitude for mathematics, logical reasoning, and problem-solving, typically fresh graduates from the 10+2 system. It caters to individuals eager to delve into complex algorithms, data analysis, and intelligent system design. Students passionate about creating futuristic technologies and contributing to India''''s AI landscape will find this specialization highly rewarding.
Why Choose This Course?
Graduates of this program can expect to secure roles such as AI Engineer, Machine Learning Specialist, Data Scientist, or Robotics Engineer in leading Indian and multinational companies. Entry-level salaries typically range from INR 4-8 lakhs per annum, with significant growth potential as experience increases. The curriculum aligns with industry certifications, enhancing career trajectories in India''''s booming AI and tech sectors.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop strong programming skills in Python and Java, which are foundational for AI. Focus on understanding data structures, algorithms, and object-oriented programming concepts thoroughly.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, NPTEL courses for programming
Career Connection
Solid coding skills are paramount for technical interviews and developing AI applications, directly impacting placement opportunities in product-based companies and startups.
Build a Strong Mathematical Base- (Semester 1-2)
Dedicate extra time to understand linear algebra, probability, and calculus. These mathematical concepts are the bedrock of machine learning and deep learning algorithms.
Tools & Resources
Khan Academy, MIT OpenCourseware, NPTEL courses on Linear Algebra and Probability
Career Connection
A deep understanding of mathematics allows for better comprehension of AI model workings, enabling advanced research and development roles later in one''''s career.
Engage in Early Project Exploration- (Semester 1-2)
Start working on small, personal projects using foundational programming skills. This could include simple game development, basic data analysis scripts, or automation tasks.
Tools & Resources
GitHub, Jupyter Notebooks, Online Python IDEs
Career Connection
Early project experience helps build a portfolio, demonstrates practical application of knowledge, and fosters problem-solving skills, which are crucial for internships.
Intermediate Stage
Participate in AI/ML Competitions- (Semester 3-5)
Join hackathons and online coding challenges focused on AI and Machine Learning. This provides practical exposure to real-world problems and competitive problem-solving.
Tools & Resources
Kaggle, Analytics Vidhya, HackerEarth challenges
Career Connection
Winning or even participating in competitions showcases your skills to potential employers, builds a strong resume, and expands your professional network for future job prospects.
Undertake Mini AI Projects & Internships- (Semester 3-5)
Apply theoretical knowledge by building machine learning models for specific datasets, or seek short-term internships/shadowing opportunities in AI-focused startups or research labs.
Tools & Resources
Google Colab, TensorFlow/PyTorch, Scikit-learn, LinkedIn for internship searches
Career Connection
Practical project experience and internships are vital for understanding industry workflows, gaining hands-on skills, and converting internships into full-time job offers.
Network with Industry Professionals- (Semester 3-5)
Attend AI conferences, webinars, and workshops. Connect with alumni and industry leaders on platforms like LinkedIn to gain insights and explore mentorship opportunities.
Tools & Resources
LinkedIn, Meetup groups for AI/ML, Industry-specific conferences in India
Career Connection
Networking opens doors to hidden job opportunities, provides career guidance, and helps build professional relationships that can be beneficial throughout your career journey.
Advanced Stage
Specialize and Deep Dive- (Semester 6-8)
Identify a niche within AI (e.g., NLP, Computer Vision, Reinforcement Learning) and pursue advanced courses, certifications, and projects in that area for deeper expertise.
Tools & Resources
Coursera/edX for specialized courses, Advanced research papers, Domain-specific libraries
Career Connection
Specialization makes you a more valuable candidate for specific roles and high-demand areas within AI, leading to better job prospects and higher salary packages.
Focus on Capstone Project & Thesis- (Semester 6-8)
Dedicate significant effort to your final year project, aiming to solve a complex, real-world problem. Document your work meticulously and prepare for robust presentations.
Tools & Resources
Academic research journals, Project management tools (Jira, Trello), Presentation software
Career Connection
A strong capstone project serves as a cornerstone of your portfolio, demonstrating your ability to lead, innovate, and deliver impactful AI solutions to potential employers.
Intensive Placement Preparation- (Semester 6-8)
Engage in rigorous aptitude training, mock interviews (technical and HR), resume building workshops, and practice coding challenges specific to AI/ML roles.
Tools & Resources
Career guidance cells, Online aptitude tests, Mock interview platforms, Peer groups
Career Connection
Thorough preparation ensures you are well-equipped to ace interviews, articulate your skills effectively, and secure placements in top-tier companies across India.
Program Structure and Curriculum
Eligibility:
- A pass in the 10+2 system of Examination with Physics and Mathematics as Compulsory subjects along with one of the Chemistry / Biotechnology / Biology / Technical Vocational subject / Computer Science / Informatics Practices / Agriculture / Engineering Graphics / Business Studies. (Minimum 45% marks in PCMB/relevant subject for General & 40% for Reserved Category)
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 |
|---|---|---|---|---|
| 19MA101T | Engineering Mathematics – I | Core | 4 | Matrices, Differential Calculus, Functions of Several Variables, Multiple Integrals, Vector Calculus |
| 19PH101T | Engineering Physics | Core | 3 | Lasers and Fibre Optics, Wave Mechanics, Crystal Physics, Dielectric and Superconducting Materials, Modern Engineering Materials |
| 19CS101T | Problem Solving and Python Programming | Core | 3 | Computational Thinking, Algorithmic Problem Solving, Python Data Structures, Control Structures, Functions and Recursion |
| 19CS102P | Problem Solving and Python Programming Laboratory | Lab | 2 | Python Programming Basics, Conditional Statements, Functions, Strings and Lists, File Operations |
| 19EE101T | Basic Electrical and Electronics Engineering | Core | 3 | DC Circuits, AC Circuits, Electrical Machines, Semiconductor Devices, Digital Electronics Fundamentals |
| 19ME101P | Engineering Graphics and Design Laboratory | Lab | 2 | Drawing Instruments, Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Basics |
| 19HS101T | English for Engineers | Core | 3 | Listening Skills, Speaking Skills, Reading Comprehension, Professional Writing, Grammar and Vocabulary |
| 19PH102P | Engineering Physics Laboratory | Lab | 1 | Lasers and Optical Fibre, Spectrometer Applications, Band Gap Measurement, Ultrasonic Interferometer, Physics Experiments |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 19MA201T | Engineering Mathematics – II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Vector Spaces, Fourier Series, Partial Differential Equations |
| 19CH201T | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry and Corrosion, Fuels and Combustion, Engineering Materials, Nanomaterials |
| 19ME201T | Engineering Mechanics | Core | 3 | Statics of Particles, Equilibrium of Rigid Bodies, Properties of Surfaces, Dynamics of Particles, Kinetics of Rigid Bodies |
| 19CS201T | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 19CS202P | Data Structures and Algorithms Laboratory | Lab | 2 | Array Operations, Linked List Implementation, Stack/Queue Applications, Tree Traversals, Graph Algorithms Implementation |
| 19GE201P | Engineering Practices Laboratory | Lab | 2 | Carpentry and Fitting, Welding and Machining, Plumbing and Sheet Metal, Electrical Wiring, Foundry Practices |
| 19CH202P | Engineering Chemistry Laboratory | Lab | 1 | Water Hardness Determination, pH Metry, Conductometry, Potentiometry, Calorimetry |
| 19HS201T | Environmental Science and Engineering | Core | 3 | Ecosystems and Biodiversity, Pollution and Control, Natural Resources, Sustainable Development, Environmental Legislation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 19MA301T | Probability and Statistics | Core | 4 | Probability Axioms, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing |
| 19CS301T | Object-Oriented Programming | Core | 3 | Classes and Objects, Inheritance, Polymorphism, Abstraction, Exception Handling |
| 19CS302P | Object-Oriented Programming Laboratory | Lab | 2 | Class Design and Implementation, Inheritance and Polymorphism, Abstract Classes and Interfaces, File I/O Operations, GUI Applications |
| 19AI301T | Introduction to Artificial Intelligence | Core | 3 | Intelligent Agents, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics |
| 19AI302P | Artificial Intelligence Laboratory | Lab | 2 | Search Algorithms Implementation, Logic Programming, Expert Systems, Game Playing AI, AI Library Usage (e.g., NLTK) |
| 19AI303T | Discrete Mathematics for AI | Core | 3 | Logic and Proofs, Set Theory, Relations and Functions, Combinatorics, Graph Theory |
| 19CS303T | Computer Organization and Architecture | Core | 3 | Basic Computer Organization, CPU Design, Memory Hierarchy, Input/Output Organization, Pipelining |
| 19CS304T | Database Management Systems | Core | 3 | Relational Model, SQL Query Language, ER Model, Normalization, Transaction Management |
| 19CS305P | Database Management Systems Laboratory | Lab | 2 | SQL Queries, Database Design, ER Diagrams, Stored Procedures, Transaction Control |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 19MA401T | Applied Linear Algebra | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Orthogonality, Matrix Decompositions |
| 19CS401T | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| 19CS402P | Design and Analysis of Algorithms Laboratory | Lab | 2 | Sorting Algorithm Implementation, Searching Algorithms, Graph Traversal Algorithms, Dynamic Programming Solutions, String Matching Algorithms |
| 19AI401T | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Ensemble Methods |
| 19AI402P | Machine Learning Laboratory | Lab | 2 | Data Preprocessing, Regression Models, Classification Algorithms, Clustering Techniques, Machine Learning Libraries (Scikit-learn) |
| 19AI403T | Natural Language Processing | Core | 3 | Text Preprocessing, Language Models, Parts-of-Speech Tagging, Syntactic Parsing, Semantic Analysis |
| 19AI404P | Natural Language Processing Laboratory | Lab | 2 | Tokenization and Stemming, Text Classification, Named Entity Recognition, Chatbot Development, NLP Libraries (NLTK, SpaCy) |
| 19CS403T | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems |
| 19HS401T | Universal Human Values and Ethics | Core | 3 | Self-Exploration, Harmony in Family, Harmony in Society, Harmony in Nature, Professional Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 19CS501T | Computer Networks | Core | 3 | Network Topologies, OSI/TCP-IP Models, Data Link Layer, Network Layer, Transport Layer |
| 19CS502P | Computer Networks Laboratory | Lab | 2 | Socket Programming, Network Simulation Tools, Packet Analysis, Router Configuration, Client-Server Communication |
| 19AI501T | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Generative Adversarial Networks (GANs) |
| 19AI502P | Deep Learning Laboratory | Lab | 2 | Image Classification, Object Detection, Text Generation, Sequence Prediction, Deep Learning Frameworks (TensorFlow/PyTorch) |
| 19AI503T | AI Ethics and Governance | Core | 3 | Ethical Principles in AI, Bias and Fairness in AI, AI and Privacy, Accountability in AI, Regulatory Frameworks for AI |
| 19AIEXX01 | Computer Vision | Professional Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Recognition, Image Segmentation, 3D Vision |
| 19AIXXS01 | Introduction to Data Science Tools | Skill Enhancement | 2 | Python for Data Science, R for Data Science, SQL for Data Analysis, Data Visualization Tools, Cloud Data Platforms |
| 19HS501T | Principles of Management | Core | 3 | Management Functions, Organizational Structures, Leadership Theories, Motivation, Decision Making |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 19AI601T | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-Learning, Deep Reinforcement Learning |
| 19AI602P | Reinforcement Learning Laboratory | Lab | 2 | OpenAI Gym Environments, Q-Learning Implementation, Policy Gradient Methods, Robotics Simulation, Game Playing Agents |
| 19AI603T | Robotics and Automation | Core | 3 | Robot Kinematics, Robot Dynamics, Sensors and Actuators, Robot Control, Trajectory Planning |
| 19AIEXX03 | Cognitive Science | Professional Elective | 3 | Perception and Action, Memory and Learning, Attention and Consciousness, Language Processing, Problem Solving and Decision Making |
| 19AIXXS02 | Advanced Python Programming for AI | Skill Enhancement | 2 | Advanced Data Structures, Object-Oriented Python, Functional Programming, Web Scraping, Concurrency and Parallelism |
| 19AIPRJ-1 | Project Work – Phase I | Project | 6 | Problem Identification, Literature Survey, System Design, Methodology Development, Preliminary Implementation |
| 19HS601T | Professional Ethics and Human Values | Core | 3 | Engineering Ethics, Professionalism, Moral Dilemmas, Rights and Responsibilities, Global Issues |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 19AIEXX05 | Genetic Algorithms and Swarm Intelligence | Professional Elective | 3 | Evolutionary Computation, Genetic Operators, Particle Swarm Optimization, Ant Colony Optimization, Real-world Applications |
| 19AIEXX07 | IoT and AI Applications | Professional Elective | 3 | IoT Architecture, Sensor Networks, Edge AI, Data Analytics for IoT, Smart Systems |
| 19AIPRJ-2 | Project Work – Phase II | Project | 6 | Advanced Implementation, Testing and Validation, Project Documentation, Results Analysis, Intermediate Presentation |
| 19OE001 | Entrepreneurship Development | Open Elective | 3 | Startup Ecosystem, Business Plan Development, Funding Strategies, Marketing and Sales, Legal Aspects for Startups |
| 19HS701T | Aptitude and Soft Skills | Core | 3 | Quantitative Aptitude, Logical Reasoning, Verbal Ability, Communication Skills, Interview Preparation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 19AIEXX09 | Conversational AI | Professional Elective | 3 | Dialogue Systems, Chatbot Development, Speech Recognition, Natural Language Generation, Virtual Assistants |
| 19OE002 | Human Rights | Open Elective | 3 | Constitutional Framework, Fundamental Rights, International Human Rights Law, Human Rights Institutions, Social Justice |
| 19AIPRJ-3 | Project Work – Phase III | Project | 6 | Final Project Development, Comprehensive Testing, Final Report Writing, Viva Voce Examination, Deployment/Demonstration |
