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B-TECH in Artificial Intelligence Machine Learning at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Tiruvallur, 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 Tiruvallur?
This B.Tech Artificial Intelligence & Machine Learning program at Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology, Chennai focuses on equipping students with expertise in intelligent systems. The curriculum emphasizes core AI/ML algorithms, deep learning, and practical applications, preparing graduates for high-demand roles in India''''s rapidly growing tech industry. It aims to develop skilled professionals capable of innovating across various sectors.
Who Should Apply?
This program is ideal for aspiring engineers with a strong aptitude for mathematics and problem-solving, typically fresh 10+2 graduates with Physics, Chemistry, and Mathematics. It also benefits working professionals seeking to pivot into cutting-edge AI roles and career changers with a foundational technical background aiming to specialize in intelligent systems.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Machine Learning Scientists, Data Scientists, or Robotics Engineers within Indian MNCs and startups. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The strong curriculum helps in aligning with industry certifications like AWS ML Specialty and Google Cloud ML Engineer, fostering robust growth trajectories.
Student Success Practices
Foundation Stage
Build Robust Programming & Math Fundamentals- (Semester 1-2)
Dedicate time to master Python and C programming, focusing on data structures and algorithms. Simultaneously, build a strong foundation in engineering mathematics, particularly linear algebra, calculus, probability, and statistics, which are crucial for AI/ML. Participate in competitive programming challenges to enhance problem-solving skills.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Khan Academy, MIT OpenCourseware
Career Connection
A solid foundation is indispensable for tackling advanced AI/ML concepts and performing well in technical interviews for placements.
Cultivate Logical Thinking & Problem Solving- (Semester 1-2)
Engage actively in problem-solving sessions and logically analyze scenarios presented in basic science and engineering courses. Form study groups to discuss complex problems and collaborate on solutions. Develop a habit of breaking down problems into smaller, manageable parts, applying concepts learned in Universal Human Values for ethical reasoning.
Tools & Resources
NPTEL courses on Problem Solving, Online puzzle platforms, Peer study groups
Career Connection
Sharp analytical and logical thinking are core competencies required for designing effective AI solutions and debugging complex systems.
Explore Basic AI Concepts & Applications- (Semester 1-2)
Beyond classroom learning, start exploring introductory resources on AI and Machine Learning. Read popular science articles, watch documentaries, and follow reputable tech blogs. Understand the ethical implications and societal impact of AI from an early stage to align with human values.
Tools & Resources
Coursera/edX introductory ML courses, YouTube channels like ''''3Blue1Brown'''', AI ethics forums
Career Connection
Early exposure helps in identifying areas of interest within AI/ML and provides context for future advanced studies and projects.
Intermediate Stage
Deep Dive into Core AI/ML Algorithms & Data Management- (Semester 3-5)
Focus on understanding the mathematical underpinnings and practical implementation of core ML algorithms (supervised, unsupervised). Gain proficiency in Database Management Systems and Operating Systems. Actively participate in lab sessions to implement algorithms from scratch and use libraries like Scikit-learn.
Tools & Resources
Kaggle for datasets, Jupyter Notebooks, MySQL/PostgreSQL, Linux OS, Scikit-learn
Career Connection
Mastery of core algorithms and data handling is fundamental for roles like Data Scientist and ML Engineer, essential for building robust AI systems.
Engage in Mini Projects & Hackathons- (Semester 3-5)
Apply theoretical knowledge by undertaking mini-projects, both as part of the curriculum and independently. Participate in hackathons and coding competitions focused on AI/ML. These provide hands-on experience, build a portfolio, and offer networking opportunities with industry professionals.
Tools & Resources
GitHub for project version control, DevPost for hackathons, Local tech meetups
Career Connection
Practical project experience is highly valued by recruiters and significantly enhances your resume for internships and entry-level positions.
Develop Object-Oriented Programming (OOP) Skills- (Semester 3-5)
Strengthen your OOP skills using languages like Java or C++ alongside Python. Understand design patterns and principles to write clean, modular, and efficient code. This is crucial for developing scalable AI applications and working in team environments, preparing for advanced software engineering roles.
Tools & Resources
LeetCode for OOP problems, Open-source projects for code review, Object-Oriented Design books
Career Connection
Strong OOP skills are vital for software development roles in AI companies and contribute to efficient code maintenance and collaboration.
Advanced Stage
Specialize in Advanced AI/ML & Deep Learning- (Semester 6-8)
Choose professional electives wisely based on your career interests (e.g., NLP, Computer Vision, Reinforcement Learning, Explainable AI). Gain expertise in deep learning frameworks like TensorFlow and PyTorch. Work on complex, multi-semester projects that integrate various AI/ML techniques for impactful solutions.
Tools & Resources
TensorFlow/PyTorch documentation, Papers With Code, Specialized online courses from NPTEL/Coursera
Career Connection
Specialization in cutting-edge areas makes you a sought-after expert, leading to advanced research or high-paying roles in niche AI domains within India''''s tech sector.
Pursue Industrial Training & Comprehensive Projects- (Semester 6-8)
Secure internships or industrial training opportunities to gain real-world exposure and understand industry best practices. Focus on your major project (Project Phase I, II, III) by selecting a challenging problem, applying innovative solutions, and documenting your work meticulously. Prepare for potential publications and patent applications.
Tools & Resources
LinkedIn for internship searches, IEEE/ACM conferences for research papers, Vel Tech''''s placement cell for guidance
Career Connection
Industrial experience and a strong final year project are critical for immediate placement success and provide a foundation for future career growth and innovation.
Focus on Career Preparedness & Ethical AI- (Semester 6-8)
Attend workshops on resume building, interview preparation, and soft skills. Network with alumni and industry professionals. Stay updated on AI ethics, governance, and responsible AI development, which are increasingly important for a trustworthy career. Practice mock interviews and aptitude tests regularly for placement readiness.
Tools & Resources
Vel Tech Career Services, Glassdoor for interview questions, AI Ethics organizations'''' guidelines (e.g., NITI Aayog''''s Responsible AI)
Career Connection
Comprehensive career preparation, combined with a strong ethical understanding, ensures you are not only technically proficient but also a responsible and employable professional in the AI industry.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics as core subjects from a recognized board, or equivalent.
Duration: 4 Years / 8 Semesters
Credits: 162 Credits
Assessment: Internal: 40% (for theory courses), 50% (for practical courses), External: 60% (for theory courses), 50% (for practical courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UEP101 | Professional English I | Humanities and Social Sciences | 3 | Listening and Speaking Skills, Reading Comprehension Strategies, Writing Techniques and Grammar, Vocabulary Building, Basic Communication for Engineers |
| UMA101 | Engineering Mathematics I | Basic Science | 4 | Matrices and Eigenvalue Problems, Differential Calculus Applications, Functions of Several Variables, Multiple Integrals, Vector Calculus Fundamentals |
| UPH101 | Engineering Physics I | Basic Science | 3 | Oscillations and Waves, Quantum Mechanics Introduction, Solid State Physics Principles, Optics and Lasers, Nuclear Physics Basics |
| UCH101 | Engineering Chemistry I | Basic Science | 3 | Water Technology and Treatment, Electrochemistry Concepts, Corrosion and its Control, Fuels and Combustion Chemistry, Environmental Chemistry Principles |
| UES101 | Environmental Science | Basic Science | 2 | Ecosystems and Biodiversity, Natural Resources Management, Environmental Pollution Control, Social Issues and the Environment, Human Population and Health |
| UPP101 | Problem Solving and Python Programming | Engineering Science | 3 | Algorithmic Problem Solving, Python Language Fundamentals, Control Flow and Functions, Data Structures in Python, File Handling and Modules |
| UGT101 | Universal Human Values I | Humanities and Social Sciences | 1 | Self-Exploration as the Process, Understanding Harmony in the Individual, Harmony in the Family and Society, Harmony in Nature, Holistic Understanding for Engineers |
| UPHL101 | Engineering Physics Lab I | Basic Science | 1 | Optical Phenomena Experiments, Material Properties Measurement, Wave Characteristics, Semiconductor Device Analysis, Basic Electronics Experimentation |
| UCHL101 | Engineering Chemistry Lab I | Basic Science | 1 | Water Quality Analysis, Acid-Base Titrations, Electrochemistry Experiments, Corrosion Rate Determination, Spectrophotometry Applications |
| UPPL101 | Problem Solving and Python Programming Lab | Engineering Science | 1 | Python Programming Exercises, Implementing Control Structures, Functions and Modules, List, Tuples, Dictionaries Operations, Debugging and Error Handling |
| UGTL101 | Universal Human Values Lab | Humanities and Social Sciences | 1 | Self-Reflection and Introspection, Group Discussions on Values, Case Studies on Ethical Dilemmas, Understanding Human Relationships, Harmony in Society and Nature |
| UGE101 | Engineering Graphics | Engineering Science | 2 | Engineering Drawing Conventions, Orthographic Projections, Projection of Solids, Sectional Views, Isometric Projections |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UEP201 | Professional English II | Humanities and Social Sciences | 3 | Advanced Reading Strategies, Business Communication Skills, Presentation Techniques, Group Discussion and Interview Skills, Report and Technical Writing |
| UMA201 | Engineering Mathematics II | Basic Science | 4 | Ordinary Differential Equations, Laplace Transforms, Vector Spaces and Linear Transformations, Eigenvalues and Eigenvectors, Complex Variables and Integration |
| UPH201 | Engineering Physics II | Basic Science | 3 | Electromagnetism and Maxwell''''s Equations, Semiconductor Physics, Lasers and Fiber Optics, Superconductivity Phenomena, Nanomaterials and Applications |
| UCH201 | Engineering Chemistry II | Basic Science | 3 | Chemical Thermodynamics, Reaction Kinetics and Catalysis, Photochemistry, Polymer Chemistry and its Types, Nanochemistry and its Synthesis |
| UCS201 | Programming in C | Engineering Science | 3 | C Language Fundamentals, Control Structures and Arrays, Functions and Pointers, Structures, Unions and Enums, File Handling and Preprocessors |
| UEE201 | Basic Electrical and Electronics Engineering | Engineering Science | 3 | DC and AC Circuits Analysis, Semiconductor Diodes and Transistors, Operational Amplifiers Characteristics, Digital Logic Gates and Boolean Algebra, Measurement and Instrumentation |
| UPHL201 | Engineering Physics Lab II | Basic Science | 1 | Magnetic Field Measurements, Laser Characteristics and Applications, Optical Fiber Communication, Semiconductor Device Fabrication, Hall Effect Experiment |
| UCHL201 | Engineering Chemistry Lab II | Basic Science | 1 | Synthesis of Organic Compounds, Chemical Kinetics Experiments, Photochemical Reactions, Polymerization Techniques, Calorimetry Experiments |
| UCSL201 | Programming in C Lab | Engineering Science | 1 | C Programming Practice, Implementing Control Structures, Array and String Manipulation, Functions and Pointers Usage, File Operations in C |
| UEEL201 | Basic Electrical and Electronics Engineering Lab | Engineering Science | 1 | Circuit Analysis Experiments, Diode and Transistor Characteristics, Operational Amplifier Applications, Logic Gate Verification, Breadboarding and Soldering Practice |
| UGA201 | Workshop Practice | Engineering Science | 2 | Carpentry and Fitting Skills, Welding Techniques, Sheet Metal Operations, Foundry Practices, Plastic Moulding and Joining |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UMA303 | Probability and Statistics for AI | Basic Science | 4 | Probability Theory and Axioms, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Regression and Correlation Analysis |
| UAI301 | Data Structures and Algorithms | Professional Core | 3 | Arrays, Stacks, Queues, Linked Lists and Trees, Graphs and Hashing, Sorting Algorithms, Searching Techniques |
| UAI302 | Object Oriented Programming | Professional Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Constructors and Destructors, Exception Handling |
| UAI303 | Digital Principles and Computer Organization | Professional Core | 3 | Boolean Algebra and Logic Gates, Combinational Circuits Design, Sequential Circuits Design, Memory Organization, CPU Design and Control Unit |
| UAI304 | Introduction to Artificial Intelligence | Professional Core | 3 | History and Foundations of AI, Intelligent Agents and Environments, Problem Solving through Search, Knowledge Representation, Machine Learning Basics |
| UEC301 | Analog and Digital Communication | Engineering Science | 3 | Amplitude Modulation Techniques, Frequency and Phase Modulation, Digital Signal Representation, Multiplexing Techniques, Data Transmission and Error Control |
| UHSL301 | Professional Skills Lab | Humanities and Social Sciences | 1 | Effective Communication Skills, Public Speaking and Presentation, Group Discussion and Interview Skills, Teamwork and Leadership, Problem-Solving and Critical Thinking |
| UAI305 | Data Structures and Algorithms Lab | Professional Core | 1 | Implementation of Linear Data Structures, Tree and Graph Traversals, Sorting and Searching Algorithms, Hashing Techniques, Algorithm Efficiency Analysis |
| UAI306 | Object Oriented Programming Lab | Professional Core | 1 | Implementing Classes and Objects, Inheritance and Polymorphism Exercises, Abstract Classes and Interfaces, Exception Handling Practice, GUI Development Basics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UMA401 | Discrete Mathematics | Basic Science | 4 | Logic and Proof Techniques, Set Theory and Relations, Functions and Sequences, Graph Theory, Algebraic Structures |
| UAI401 | Operating Systems | Professional Core | 3 | Operating System Structures, Process Management and Scheduling, Memory Management Techniques, File Systems, Deadlocks and Concurrency Control |
| UAI402 | Database Management Systems | Professional Core | 3 | Data Models and Architectures, Relational Algebra and SQL, Database Design and Normalization, Transaction Management, Concurrency Control and Recovery |
| UAI403 | Design and Analysis of Algorithms | Professional Core | 3 | Algorithm Analysis and Complexity, Divide and Conquer Strategy, Dynamic Programming, Greedy Algorithms, NP-Completeness and Approximation Algorithms |
| UAI404 | Machine Learning | Professional Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Reinforcement Learning Basics, Model Evaluation and Validation, Ensemble Methods |
| UAI405 | Computer Networks | Professional Core | 3 | Network Topologies and OSI Model, TCP/IP Protocol Suite, Routing and Congestion Control, Application Layer Protocols, Network Security Fundamentals |
| UAI406 | Database Management Systems Lab | Professional Core | 1 | SQL Queries and Operations, Database Schema Design, PL/SQL Programming, Form and Report Generation, Transaction Control |
| UAI407 | Operating Systems Lab | Professional Core | 1 | Shell Scripting, Process Management in Linux, CPU Scheduling Algorithms, Memory Allocation Techniques, Inter-Process Communication |
| UAI408 | Machine Learning Lab | Professional Core | 1 | Implementing ML Algorithms (Scikit-learn), Data Preprocessing and Visualization, Model Training and Evaluation, Feature Engineering, Introduction to TensorFlow/PyTorch |
| UAI409 | Mini Project | Project Work | 1 | Problem Identification and Scope Definition, System Design and Planning, Implementation and Testing, Technical Report Writing, Presentation of Project Work |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UMA501 | Optimization Techniques for AI | Basic Science | 4 | Linear and Non-Linear Programming, Dynamic Programming, Evolutionary Algorithms, Gradient Descent Methods, Metaheuristics |
| UAI501 | Data Warehousing and Data Mining | Professional Core | 3 | Data Warehousing Concepts, OLAP and Multidimensional Models, Data Preprocessing, Association Rule Mining, Classification and Clustering Techniques |
| UAI502 | Computer Graphics and Multimedia | Professional Core | 3 | 2D and 3D Transformations, Viewing and Projections, Shading and Rendering Techniques, Animation Principles, Multimedia Data Formats |
| UAI503 | Natural Language Processing | Professional Core | 3 | Text Preprocessing and Tokenization, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation Basics |
| UAI504 | Deep Learning | Professional Core | 3 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and Generative Adversarial Networks, Deep Learning Frameworks (TensorFlow/PyTorch) |
| PEI | Professional Elective I | Professional Elective | 3 | Advanced AI Applications, Image and Video Processing, Big Data Technologies, Augmented/Virtual Reality Fundamentals, Cognitive Computing Systems |
| UAI510 | Data Warehousing and Data Mining Lab | Professional Core | 1 | ETL Process Implementation, OLAP Operations, Association Rule Mining, Classification and Clustering Algorithms, Data Visualization Tools |
| UAI511 | Deep Learning Lab | Professional Core | 1 | Implementing Neural Networks, Training CNNs for Image Recognition, RNNs for Sequence Data, Generative Adversarial Networks Practice, Using TensorFlow/Keras/PyTorch |
| UAI512 | Mini Project II | Project Work | 1 | Advanced Problem Formulation, Detailed System Design, Implementation with Modern Tools, Testing and Evaluation, Technical Documentation and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAI601 | Soft Computing | Professional Core | 3 | Fuzzy Logic Systems, Artificial Neural Networks, Genetic Algorithms, Swarm Intelligence Algorithms, Hybrid Soft Computing Techniques |
| UAI602 | Reinforcement Learning | Professional Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning (Q-learning, SARSA), Policy Gradient Methods |
| UAI603 | Cloud Computing | Professional Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization Technology, Cloud Deployment Models, Cloud Security and Data Privacy, AWS/Azure/GCP Services |
| PEII | Professional Elective II | Professional Elective | 3 | Blockchain Technologies, Internet of Things (IoT), Human Computer Interaction, Agent Based Intelligent Systems, Quantum Machine Learning Concepts |
| PEIII | Professional Elective III | Professional Elective | 3 | Cyber Physical Systems, Game Theory, Robotics Process Automation (RPA), Business Intelligence, AI Ethics and Governance |
| OEI | Open Elective I | Open Elective | 3 | Varies based on student choice and available courses from other departments. |
| UAI614 | Reinforcement Learning Lab | Professional Core | 1 | Implementing Q-learning, SARSA Algorithm, Deep Reinforcement Learning Basics, Using OpenAI Gym Environments, Policy Gradient Implementations |
| UAI615 | Soft Computing Lab | Professional Core | 1 | Fuzzy Logic Toolbox Implementation, Neural Network Training, Genetic Algorithm Applications, Swarm Intelligence Problem Solving, Hybrid System Design |
| UAI616 | Project Phase I | Project Work | 2 | Extensive Literature Survey, Problem Definition and Scope, Detailed System Design, Preliminary Implementation, Project Proposal and Planning |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAI701 | Explainable AI | Professional Core | 3 | Interpretability vs Explainability, Local and Global Explanations (LIME, SHAP), Fairness and Bias in AI, Model Debugging and Transparency, Ethical AI Principles |
| PEIV | Professional Elective IV | Professional Elective | 3 | Data Security Concepts, Data Privacy Regulations, Digital Forensics Techniques, Ethical Hacking Methodologies, Data Science for Healthcare |
| PEV | Professional Elective V | Professional Elective | 3 | AI in Financial Applications, Social Media Analytics, AI for Agricultural Robotics, AI for Sustainable Development, Cognitive Robotics and HRI |
| OEII | Open Elective II | Open Elective | 3 | Varies based on student choice and available courses from other departments. |
| UAI712 | Industrial Training / Internship | Project Work | 2 | Real-world Industry Exposure, Application of AI/ML Concepts, Project Management in Industry, Professional Communication Skills, Internship Report and Presentation |
| UAI713 | Project Phase II | Project Work | 6 | Advanced System Implementation, Module Integration and Testing, Performance Evaluation, Comprehensive Documentation, Interim Project Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAI801 | Project Phase III | Project Work | 12 | Final System Development and Refinement, Extensive Testing and Validation, Research Paper/Thesis Writing, Final Project Defense, Demonstration of Project Outcome |
| PEVI | Professional Elective VI | Professional Elective | 3 | Computer Vision Algorithms, Biometric Security Systems, Speech and Audio Processing, Swarm Intelligence Applications, Quantum Computing Fundamentals |
