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B-TECH in Artificial Intelligence Machine Learning 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 & Machine Learning at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvallur?
This Artificial Intelligence & Machine Learning program at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology focuses on equipping students with a robust foundation in AI/ML principles and applications. Catering to India''''s burgeoning tech industry, the program emphasizes hands-on experience and problem-solving, preparing graduates to innovate across various sectors. Its comprehensive curriculum covers fundamental theories alongside cutting-edge technologies.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and logical reasoning, seeking entry into high-growth AI/ML careers. It also suits working professionals eager to upskill in emerging technologies and career changers transitioning into data-driven roles within the Indian IT and manufacturing sectors. Aspiring researchers and innovators in the AI domain will find the curriculum particularly engaging.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Specialist, Data Scientist, or AI Researcher. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals earning significantly more (INR 10-25+ LPA). The program aligns with industry demands, fostering skills for professional certifications in areas like deep learning and big data analytics, crucial for growth in Indian companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice Python and Java programming concepts through daily coding challenges. Focus on understanding data structures and algorithms thoroughly. Engage in peer coding sessions to debug and learn from others'''' approaches.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, JavaTpoint
Career Connection
Strong programming skills are the bedrock for any AI/ML role, ensuring eligibility for entry-level developer or analyst positions in Indian tech companies.
Build Strong Mathematical & Statistical Foundations- (Semester 1-3)
Pay extra attention to Engineering Mathematics and Probability & Statistics courses. Solve a wide variety of problems, understand underlying theories, and apply concepts to basic data sets. Form study groups to tackle complex problems collectively.
Tools & Resources
Khan Academy, NPTEL courses on probability, standard textbooks
Career Connection
A solid grasp of math and stats is crucial for understanding ML algorithms, which is a key requirement for data scientist and ML engineer roles.
Engage in Basic Tech Projects & Hackathons- (Semester 2-3)
Participate in campus mini-projects or local hackathons to apply classroom knowledge. Start with simple projects like basic calculators, data analysis scripts, or small web applications. This helps in building a practical portfolio from the beginning.
Tools & Resources
GitHub, local hackathon announcements, college project clubs
Career Connection
Early project experience demonstrates initiative and practical skills, setting students apart during campus recruitment for junior developer roles.
Intermediate Stage
Deep Dive into AI/ML Core Concepts- (Semester 4-5)
Beyond regular coursework, explore advanced topics in AI and Machine Learning through online courses from platforms like Coursera, edX, or NPTEL. Focus on understanding algorithm mechanics, performance metrics, and model optimization techniques.
Tools & Resources
Coursera''''s ''''Machine Learning'''' by Andrew Ng, NPTEL''''s ''''Introduction to Machine Learning'''', Kaggle for datasets and competitions
Career Connection
This specialization directly prepares students for core ML Engineer and Data Scientist positions, requiring a deep conceptual understanding.
Pursue Internships and Industry Exposure- (Semester 4-6)
Actively seek out internships (summer or part-time) with startups, mid-sized companies, or MNCs in India. Focus on roles involving data analysis, AI model development, or related software engineering. Attend industry workshops and webinars.
Tools & Resources
LinkedIn, Internshala, college placement cell, company career pages
Career Connection
Internships provide invaluable real-world experience, a critical differentiator for placements and often lead to pre-placement offers in companies like TCS, Wipro, Infosys.
Build a Robust Project Portfolio- (Semester 4-6)
Work on 2-3 substantial AI/ML projects, either individually or in teams, showcasing different algorithms and applications (e.g., image classification, natural language processing, recommendation systems). Document your work thoroughly on GitHub.
Tools & Resources
TensorFlow, Keras, PyTorch, Scikit-learn, Google Colab, GitHub
Career Connection
A strong portfolio is crucial for demonstrating practical skills to recruiters, significantly boosting chances for high-paying roles in AI-driven companies.
Advanced Stage
Specialize and Innovate through Capstone Project- (Semester 7-8)
Choose a challenging final year project that applies advanced AI/ML techniques to a real-world problem, potentially in collaboration with an industry partner. Aim for novel solutions or significant improvements on existing methods.
Tools & Resources
Advanced research papers, domain-specific libraries, mentorship from faculty/industry experts
Career Connection
A high-quality capstone project can serve as a powerful resume builder, demonstrating advanced problem-solving and innovation, attracting top recruiters for R&D or advanced engineering roles.
Master Placement-Specific Skills & Networking- (Semester 6-8)
Dedicate significant time to placement preparation, focusing on company-specific aptitude tests, technical interviews (data structures, algorithms, ML concepts), and soft skills. Network with alumni and industry professionals through LinkedIn and college events.
Tools & Resources
Mock interviews, online aptitude test platforms, alumni mentoring programs, professional networking events
Career Connection
Excellent preparation ensures success in the competitive Indian job market, securing roles with leading tech companies and startups at competitive salary packages.
Explore Advanced Certifications and Research- (Semester 7-8)
Consider pursuing advanced certifications in niche AI/ML areas (e.g., AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer) or contributing to research papers. This demonstrates a commitment to lifelong learning and expertise.
Tools & Resources
Official certification guides, academic journals, research labs at the university
Career Connection
Advanced certifications and research experience can open doors to specialized, high-impact roles, and even opportunities for higher studies or roles in academic/industrial research in India and abroad.
Program Structure and Curriculum
Eligibility:
- Candidates should have passed 10+2 with a minimum aggregate of 45% (40% for reserved category) in Mathematics, Physics, and Chemistry/Biology/Biotechnology/Technical Vocational subject from a recognized board.
Duration: 8 Semesters / 4 Years
Credits: 151 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BT101 | Communicative English | Core | 3 | Listening Skills, Speaking Skills, Reading Skills, Writing Skills, Language Development |
| 11BT102 | Engineering Mathematics – I | Core | 4 | Differential Calculus, Integral Calculus, Ordinary Differential Equations, Multiple Integrals, Vector Calculus |
| 11BT103 | Engineering Physics | Core | 3 | Quantum Physics, Wave Optics, Lasers and Fibre Optics, Crystal Physics, Electrical and Magnetic Properties of Materials |
| 11BT104 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry, Corrosion, Fuel Technology, Polymer Chemistry |
| 11BT105 | Problem Solving and Python Programming | Core | 3 | Algorithmic Problem Solving, Python Fundamentals, Control Flow, Functions, Data Structures |
| 11BT106 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Perspective Projections, Auto CAD basics |
| 11BT107 | Communicative English Laboratory | Lab | 1 | Phonetics, Role play, Group Discussions, Presentation Skills, Interview Skills |
| 11BT108 | Engineering Physics and Chemistry Laboratory | Lab | 1 | Spectrometer, Ultrasonic Interferometer, Young''''s Modulus, pH measurement, Potentiometer |
| 11BT109 | Problem Solving and Python Programming Laboratory | Lab | 1 | Python Programs for problem solving, Conditional statements, Functions, Lists, Dictionaries |
| 11BT10A | Engineering Practices Laboratory | Lab | 1 | Carpentry, Welding, Sheet Metal, Plumbing, Electrical Wiring |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BT201 | Professional English | Core | 3 | Technical Communication, Formal Reports, Emails, Presentations, Soft Skills |
| 11BT202 | Engineering Mathematics – II | Core | 4 | Matrices, Vector Spaces, Eigenvalues, Numerical Methods, Transform Techniques |
| 11BT203 | Physics of Materials | Core | 3 | Semiconductor Physics, Dielectric Materials, Magnetic Materials, Superconducting Materials, Nanomaterials |
| 11BT204 | Environmental Science and Engineering | Core | 3 | Ecosystems, Biodiversity, Pollution, Renewable Energy, Environmental Management |
| 11BT205 | Data Structures | Core | 3 | Array, Stacks, Queues, Linked Lists, Trees, Graphs, Hashing |
| 11BT206 | Electrical and Electronics Engineering | Core | 3 | DC Circuits, AC Circuits, Diodes, Transistors, Operational Amplifiers |
| 11BT207 | Data Structures Laboratory | Lab | 1 | Implementation of Stacks, Queues, Linked Lists, Tree Traversal, Graph Algorithms |
| 11BT208 | Electrical and Electronics Engineering Laboratory | Lab | 1 | Ohm''''s Law, Kirchhoff''''s Laws, PN Junction Diode, Zener Diode, Transistor Characteristics |
| 11BT209 | Science and Engineering Laboratory | Lab | 1 | Specific resistivity, Band gap, Hall Effect, Solar cell characteristics, Optical fiber communication |
| 11BT20A | Value Education, Human Rights and Ethics | Non-credit | 0 | Values, Ethics, Human Rights, Professional Ethics, Social Responsibility |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BT301 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables, Distributions, Hypothesis Testing, Correlation and Regression |
| 11BT302 | Object Oriented Programming using Java | Core | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling, Collections |
| 11BT303 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| 11BT304 | Computer Architecture | Core | 3 | CPU Organization, Instruction Set, Pipelining, Memory Hierarchy, I/O Organization |
| 11BT305 | Database Management Systems | Core | 3 | Relational Model, SQL, ER Model, Normalization, Transaction Management, Concurrency Control |
| 11BT306 | Object Oriented Programming using Java Laboratory | Lab | 1 | Java Programs for OOP, Inheritance, Polymorphism, GUI applications, Database connectivity |
| 11BT307 | Database Management Systems Laboratory | Lab | 1 | SQL queries, DDL, DML, Joins, Triggers, Procedures, Front-end integration |
| 11BT308 | Design and Analysis of Algorithms Laboratory | Lab | 1 | Implementation of sorting, searching, greedy, dynamic programming algorithms, Graph algorithms |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BT401 | Discrete Mathematics | Core | 4 | Logic, Set Theory, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| 11BT402 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, I/O Systems |
| 11BT403 | Computer Networks | Core | 3 | Network Topologies, OSI Model, TCP/IP, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| 11BT404 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving, Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems |
| 11BT405 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Model Evaluation, Deep Learning Introduction |
| 11BT406 | Operating Systems Laboratory | Lab | 1 | Linux Commands, Shell Scripting, Process Management, CPU Scheduling, Deadlock Prevention, Memory Allocation |
| 11BT407 | Artificial Intelligence and Machine Learning Laboratory | Lab | 1 | Python for AI/ML, Search algorithms, Classification algorithms, Regression algorithms, Clustering algorithms |
| 11BT408 | Quantitative Aptitude and Logical Reasoning | Skill Development | 1 | Number System, Algebra, Time & Work, Data Interpretation, Blood Relations, Syllogism |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BT501 | Principles of Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| 11BT502 | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop Ecosystem, HDFS, MapReduce, Spark, Data Warehousing, Data Streaming |
| 11BT503 | Deep Learning | Core | 3 | Neural Networks, Perceptron, Backpropagation, CNN, RNN, GANs, Transfer Learning |
| 11BTPE | Professional Elective - I | Elective | 3 | Varies based on selected elective from a pool of specialized topics such as Natural Language Processing, Computer Vision, Reinforcement Learning, Cognitive Computing, Robotics, Speech Recognition |
| 11BTOE | Open Elective - I | Elective | 3 | Varies widely based on chosen inter-departmental elective |
| 11BT504 | Big Data Analytics Laboratory | Lab | 1 | Hadoop setup, HDFS operations, MapReduce programming, Spark applications, Data ingestion tools |
| 11BT505 | Deep Learning Laboratory | Lab | 1 | TensorFlow/Keras, CNN implementation, RNN for sequence data, Image classification, Text generation |
| 11BT506 | Mini Project – I | Project | 1 | Problem Identification, Literature Survey, System Design, Implementation, Testing, Report Writing |
| 11BT507 | General Aptitude | Skill Development | 1 | Verbal Ability, Critical Reasoning, Abstract Reasoning, Data Sufficiency, General Knowledge |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BT601 | Cryptography and Network Security | Core | 3 | Cryptographic Algorithms, Symmetric Key, Asymmetric Key, Hashing, Digital Signatures, Network Security Protocols |
| 11BT602 | Data Warehousing and Data Mining | Core | 3 | Data Warehouse Architecture, OLAP, Data Preprocessing, Association Rules, Classification, Clustering |
| 11BT603 | Computer Vision | Core | 3 | Image Processing, Feature Extraction, Object Detection, Image Segmentation, Image Recognition, Motion Analysis |
| 11BTPE | Professional Elective - II | Elective | 3 | Varies based on selected elective from a pool of specialized topics such as Ethical Hacking, Cyber Forensics, Cloud Computing, IoT, Blockchain, Quantum Computing, Human Computer Interaction |
| 11BTOE | Open Elective - II | Elective | 3 | Varies widely based on chosen inter-departmental elective |
| 11BT604 | Data Warehousing and Data Mining Laboratory | Lab | 1 | Data preprocessing tools, Weka, RapidMiner, Association rule mining, Classification algorithms, Clustering algorithms |
| 11BT605 | Computer Vision Laboratory | Lab | 1 | OpenCV, Image manipulation, Edge detection, Feature matching, Object recognition, Image stitching |
| 11BT606 | Mini Project – II | Project | 1 | Advanced Project Development, Design, Implementation, Testing, Documentation, Presentation |
| 11BT607 | Soft Skills | Skill Development | 1 | Communication Skills, Teamwork, Leadership, Time Management, Stress Management, Interview Preparation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BT701 | Internet of Things | Core | 3 | IoT Architecture, Sensors and Actuators, IoT Protocols, Cloud Integration, Edge Computing, IoT Security |
| 11BT702 | Robotics and Automation | Core | 3 | Robot Kinematics, Dynamics, Actuators, Sensors, Robot Programming, Industrial Automation, AI in Robotics |
| 11BTPE | Professional Elective - III | Elective | 3 | Varies based on selected elective from a pool of specialized topics such as Recommender Systems, Text Analytics, Explainable AI, Game Theory, Bio-inspired AI, Digital Image Processing |
| 11BTPE | Professional Elective - IV | Elective | 3 | Varies based on selected elective from a pool of specialized topics such as Recommender Systems, Text Analytics, Explainable AI, Game Theory, Bio-inspired AI, Digital Image Processing |
| 11BTOE | Open Elective - III | Elective | 3 | Varies widely based on chosen inter-departmental elective |
| 11BT703 | Internet of Things Laboratory | Lab | 1 | Arduino/Raspberry Pi programming, Sensor interfacing, Cloud platforms (AWS IoT/Azure IoT), Data visualization |
| 11BT704 | Project – Phase I | Project | 3 | Research Methodology, Problem Statement, Literature Review, Project Proposal, Initial Design, Feasibility Study |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 11BTPE | Professional Elective - V | Elective | 3 | Varies based on selected elective from a pool of specialized topics such as Ethical Hacking, Cyber Forensics, Cloud Computing, IoT, Blockchain, Quantum Computing, Human Computer Interaction |
| 11BTOE | Open Elective - IV | Elective | 3 | Varies widely based on chosen inter-departmental elective |
| 11BT801 | Project – Phase II | Project | 6 | System Implementation, Testing, Optimization, Thesis Writing, Project Defense, Final Presentation |
