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B-TECH-B-E in Artificial Intelligence And Machine Learning at Saveetha Institute of Medical and Technical Sciences
Chennai, Tamil Nadu
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About the Specialization
What is Artificial Intelligence and Machine Learning at Saveetha Institute of Medical and Technical Sciences Chennai?
This B.Tech Artificial Intelligence and Machine Learning program at Saveetha Institute of Medical and Technical Sciences focuses on equipping students with a robust foundation in AI, ML, and Data Science. It emphasizes theoretical concepts alongside practical applications, preparing graduates for the rapidly evolving technological landscape in India. The curriculum is designed to foster innovation and problem-solving skills, addressing the significant demand for AI professionals across various Indian industries.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and computer science, aspiring to build careers in cutting-edge AI fields. It also suits individuals passionate about developing intelligent systems, analyzing complex data, and innovating solutions for real-world challenges. Students seeking entry into roles like AI Engineer, Machine Learning Specialist, or Data Scientist within the Indian tech ecosystem will find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect to secure diverse roles such as AI Engineer, Data Scientist, Machine Learning Engineer, NLP Specialist, or Computer Vision Engineer in India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly higher. The program aligns with industry certifications and provides a strong foundation for advanced studies or entrepreneurship in the burgeoning Indian AI sector.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time to thoroughly understand and practice C/C++ and Data Structures. This forms the bedrock for all advanced AI/ML concepts. Solve at least 3-5 programming problems daily on various platforms to build logical thinking and coding proficiency.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, NPTEL courses on Data Structures
Career Connection
Strong programming skills are non-negotiable for AI/ML roles, crucial for coding interviews at product-based companies and efficient algorithm implementation.
Build a Strong Mathematical Base- (Semester 1-3)
Focus intently on Engineering Mathematics subjects like Calculus, Linear Algebra, Probability and Statistics. These are the theoretical pillars of Machine Learning. Understand derivations, work through examples, and apply concepts to small problems to solidify your understanding.
Tools & Resources
Khan Academy, MIT OpenCourseware, NPTEL, 3Blue1Brown YouTube channel, textbooks by Gilbert Strang (Linear Algebra)
Career Connection
A solid mathematical understanding is vital for comprehending ML algorithms, debugging models, understanding research papers, and pursuing advanced studies or research in AI/ML.
Engage in Peer Learning & Collaborative Projects- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and work on small programming challenges or mini-projects together. Explaining concepts to others solidifies your own understanding and develops teamwork skills.
Tools & Resources
GitHub for collaborative coding, Discord/WhatsApp groups for discussion, college lab facilities and mentorship programs
Career Connection
Teamwork, communication, and problem-solving in a group setting are critical soft skills highly valued by Indian tech companies, enhancing your readiness for real-world development teams.
Intermediate Stage
Dive Deep into Machine Learning Frameworks- (Semester 4-5)
Beyond theoretical understanding, gain extensive hands-on experience with popular ML libraries and frameworks like Scikit-learn, TensorFlow, and PyTorch. Implement various algorithms from scratch and effectively utilize library functions for complex tasks.
Tools & Resources
Google Colab, Kaggle notebooks, Official documentation of TensorFlow/PyTorch, Coursera/Udemy courses (e.g., Andrew Ng''''s ML course)
Career Connection
Practical proficiency in these tools is a primary requirement for most AI/ML engineering roles, enabling immediate contribution to projects and demonstrating job-readiness.
Participate in Data Science Competitions & Hackathons- (Semester 4-6)
Actively participate in online data science competitions (e.g., Kaggle) and college/inter-college hackathons. This applies theoretical knowledge to real-world datasets, sharpens problem-solving under pressure, and exposes you to diverse challenges.
Tools & Resources
Kaggle.com, Analytics Vidhya, local college hackathon committees, Devfolio
Career Connection
Such participation demonstrates practical skills, builds a strong portfolio of applied projects, and connects you with industry professionals, significantly enhancing placement prospects in India.
Develop Personal AI/ML Projects & Portfolio- (Semester 3-5)
Start building your own end-to-end AI/ML projects beyond classroom assignments. This could be a recommendation system, an image classifier, or an NLP application. Document your process thoroughly and showcase your work on platforms like GitHub or personal websites.
Tools & Resources
GitHub, Medium/personal blog for project documentation, datasets from UCI ML Repository, Kaggle, Streamlit/Gradio for showcasing demos
Career Connection
A strong and diverse project portfolio is crucial for attracting recruiters and effectively showcasing your practical abilities and problem-solving mindset during interviews for AI/ML roles in India.
Advanced Stage
Secure Internships & Industry Exposure- (Semester 6-7 (during breaks or dedicated period))
Actively seek and undertake internships (minimum 1-2) at reputable tech companies, startups, or research labs specializing in AI/ML. Focus on gaining hands-on industry experience, understanding real-world project pipelines, and applying academic knowledge.
Tools & Resources
LinkedIn, Internshala, college placement cell, company career pages, networking events
Career Connection
Internships provide invaluable real-world experience, build professional networks, and often lead to pre-placement offers, significantly boosting your career launch in the competitive Indian tech industry.
Specialize and Deepen Knowledge- (Semester 6-8)
Based on your interest (e.g., Computer Vision, NLP, Reinforcement Learning, Ethical AI), strategically choose relevant professional electives and focus on mastering that domain. Read cutting-edge research papers and actively follow key developments in your chosen area.
Tools & Resources
arXiv, Google Scholar, specific conferences (CVPR, NeurIPS, ACL), advanced online courses from platforms like edX or Coursera
Career Connection
Specialization makes you a valuable asset for specific industry roles and niche areas within Indian tech companies, leading to more tailored opportunities and potentially higher compensation.
Prepare for Placements and Professional Networking- (Semester 7-8)
Actively prepare for technical interviews (Data Structures & Algorithms, ML concepts, system design), behavioral questions, and HR rounds. Attend industry events, seminars, and networking sessions to connect with professionals and potential employers, leveraging the college alumni network.
Tools & Resources
InterviewBit, LeetCode, company-specific interview guides, LinkedIn, mock interviews with peers and mentors
Career Connection
Comprehensive preparation ensures readiness for campus placements and off-campus opportunities, securing desirable job roles in India''''s competitive AI/ML market and establishing a professional foundation.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics, Mathematics and Chemistry/Computer Science/Electronics/Information Technology/Biology/Informatics Practices/Biotechnology/Technical Vocational subject as compulsory subjects with at least 45% marks (40% in case of candidates belonging to reserved category) in the above subjects taken together.
Duration: 8 semesters / 4 years
Credits: 170 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191MA101 | Engineering Mathematics - I | Core | 4 | Matrices, Differential Calculus, Functions of Several Variables, Multiple Integrals, Vector Calculus |
| 191PH101 | Engineering Physics | Core | 4 | Properties of Matter, Optics and Lasers, Thermal Physics, Quantum Physics, Materials Science |
| 191CY101 | Engineering Chemistry | Core | 4 | Water Technology, Electrochemistry, Corrosion, Fuels and Combustion, Polymer Chemistry |
| 191EE101 | Basic Electrical and Electronics Engineering | Core | 4 | DC Circuits, AC Circuits, Electrical Machines, Semiconductor Devices, Digital Electronics |
| 191CS101 | Problem Solving and Programming using C | Core | 3 | C Language Fundamentals, Control Statements, Functions, Arrays, Pointers, Structures |
| 191HS101 | Communicative English | Core | 2 | Listening Skills, Speaking Skills, Reading Skills, Writing Skills, Grammar |
| 191GE101 | Engineering Graphics | Core | 1 | Plane Curves, Projections of Points, Lines, Planes, Solids, Section of Solids, Development of Surfaces |
| 191CS111 | Problem Solving and Programming using C Lab | Lab | 2 | C Programming Practice, Debugging, Array Operations, Function Implementation, Pointer Applications |
| 191GE111 | Physical Education | Mandatory Non-Credit | 0 | Fitness Activities, Team Sports, Individual Sports, Yoga, Health and Wellness |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191MA201 | Engineering Mathematics - II | Core | 4 | Complex Numbers, Laplace Transforms, Fourier Series, Partial Differential Equations, Z-Transforms |
| 191CH201 | Environmental Science and Engineering | Core | 3 | Ecosystems, Environmental Pollution, Natural Resources, Biodiversity, Sustainable Development |
| 191IT201 | Data Structures | Core | 4 | Arrays, Stacks, Queues, Linked Lists, Trees, Graphs, Searching and Sorting, Hashing |
| 191EC201 | Digital Logic and Computer Organization | Core | 4 | Boolean Algebra, Logic Gates and Circuits, Combinational Logic, Sequential Logic, Computer Architecture Fundamentals |
| 191CS201 | Object Oriented Programming using C++ | Core | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Virtual Functions and Templates |
| 191HS201 | Professional Communication | Core | 2 | Technical Writing, Presentations, Group Discussions, Interview Skills, Business Correspondence |
| 191IT211 | Data Structures Lab | Lab | 2 | Implementation of Stacks, Queues, Linked List Operations, Tree Traversals, Graph Algorithms, Sorting and Searching Techniques |
| 191CS211 | Object Oriented Programming using C++ Lab | Lab | 2 | C++ Program Implementation, Class Design, Inheritance Application, Polymorphism Usage, File Handling |
| 191GE211 | NSS/NCC/YRC | Mandatory Non-Credit | 0 | Community Service, Discipline and Leadership, Social Responsibility, Environmental Awareness, Youth Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191MA301 | Engineering Mathematics – III (Probability and Statistics) | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing |
| 191CS301 | Database Management Systems | Core | 4 | Relational Model, SQL Queries, ER Modeling, Normalization, Transaction Management |
| 191CS302 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| 191CS303 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems |
| 191AI301 | Foundations of Artificial Intelligence | Core | 3 | AI History and Scope, Problem Solving Agents, Heuristic Search Techniques, Knowledge Representation, Expert Systems |
| 191HS301 | Technical English | Core | 2 | Technical Reports, Proposals and Research Papers, Email Writing, Presentation Skills, Effective Communication |
| 191CS311 | Database Management Systems Lab | Lab | 2 | SQL Queries, Database Design, PL/SQL Programming, Report Generation, Database Connectivity |
| 191AI311 | Artificial Intelligence Lab | Lab | 2 | Python Programming for AI, AI Library Usage, Search Algorithm Implementation, Knowledge Representation, Logic Programming |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191AI401 | Machine Learning Essentials | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation |
| 191AI402 | Data Science for AI | Core | 4 | Data Preprocessing, Exploratory Data Analysis, Data Visualization, Feature Engineering, Big Data Concepts |
| 191CS401 | Computer Networks | Core | 4 | Network Topologies, OSI Model, TCP/IP Protocol Suite, Routing Protocols, Network Security Basics |
| 191CS402 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing, Project Management |
| 191AI403 | Natural Language Processing | Core | 3 | Text Preprocessing, N-grams and Language Models, Word Embeddings, Part-of-Speech Tagging, Sentiment Analysis |
| 191AI411 | Machine Learning Lab | Lab | 2 | Python for ML, Scikit-learn, Model Training, Evaluation Metrics, Data Visualization |
| 191AI412 | Data Science for AI Lab | Lab | 2 | Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, Data Cleaning, Feature Scaling, Statistical Analysis |
| 191GE401 | Indian Constitution | Mandatory Non-Credit | 0 | Constitutional Framework, Fundamental Rights and Duties, Directive Principles, Parliamentary System, Judiciary |
| 191GE402 | Professional Ethics | Mandatory Non-Credit | 0 | Ethical Theories, Engineering Ethics, Moral Autonomy, Code of Conduct, Safety and Risk |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191AI501 | Deep Learning | Core | 4 | Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Backpropagation, Transfer Learning |
| 191AI502 | Reinforcement Learning | Core | 3 | Markov Decision Process, Q-Learning, SARSA Algorithm, Policy Gradients, Deep Reinforcement Learning |
| 191AI503 | Big Data Analytics | Core | 4 | Hadoop Ecosystem, Spark Framework, MapReduce, HDFS, Stream Processing |
| 191CS501 | Web Technology | Core | 3 | HTML, CSS, JavaScript, Client-Side Scripting, Web Servers, Client-Server Architecture, Web Security Fundamentals |
| 191AI901 | Distributed Artificial Intelligence (Professional Elective - I) | Elective | 3 | Multi-Agent Systems, Agent Communication, Distributed Problem Solving, Collective Intelligence, Swarm Intelligence |
| 191AI511 | Deep Learning Lab | Lab | 2 | TensorFlow/PyTorch Implementation, CNN Implementation, RNN Implementation, Hyperparameter Tuning, Model Visualization |
| 191AI512 | Reinforcement Learning Lab | Lab | 2 | OpenAI Gym Environments, Q-Learning Implementation, SARSA Implementation, Policy Gradient Algorithms, Deep RL Practice |
| 191GE501 | Essence of Indian Traditional Knowledge | Mandatory Non-Credit | 0 | Vedic Sciences, Traditional Indian Arts, Yoga and Ayurveda, Indian Philosophy, Ethical Values |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191AI601 | AI in Robotics | Core | 4 | Robot Kinematics, Path Planning, Robot Vision, Human-Robot Interaction, Machine Learning in Robotics |
| 191AI602 | Computer Vision | Core | 4 | Image Processing, Feature Detection, Object Recognition, Image Segmentation, Deep Learning for Vision |
| 191AI603 | Ethical AI | Core | 3 | AI Ethics Principles, Bias in AI, Fairness and Accountability, Transparency and Interpretability, Privacy and Data Protection |
| 191AI906 | Intelligent Agents and Multi-Agent Systems (Professional Elective - II) | Elective | 3 | Agent Architectures, Rational Agents, Agent Cooperation, Communication Languages, Game Theory and MAS |
| 191OE901 | Fundamentals of Management (Open Elective - I) | Elective | 3 | Principles of Management, Planning, Organizing, Staffing and Directing, Controlling |
| 191AI611 | AI in Robotics Lab | Lab | 2 | ROS Basics, Robot Simulation, Vision-based Navigation, Robotic Arm Control, Autonomous Movement |
| 191AI612 | Computer Vision Lab | Lab | 2 | OpenCV Library, Image Manipulation, Object Detection, Face Recognition, Deep Learning for Vision Applications |
| 191AI681 | Mini Project - I | Project | 2 | Project Planning, Literature Review, System Design, Implementation, Reporting and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191AI911 | Advanced Deep Learning (Professional Elective - III) | Elective | 3 | Generative Models (GANs, VAEs), Attention Mechanisms, Graph Neural Networks, Self-Supervised Learning, Reinforcement Learning with Deep Models |
| 191AI916 | Cognitive Computing (Professional Elective - IV) | Elective | 3 | Human Cognition Models, Cognitive Architectures, Affective Computing, Brain-Inspired AI, Cognitive Robotics |
| 191AI921 | Cloud Computing for AI (Professional Elective - V) | Elective | 3 | Cloud Platforms (AWS, Azure, GCP), SaaS/PaaS/IaaS, Distributed AI Training, Cloud Security for AI, Serverless Computing for AI |
| 191OE905 | Intellectual Property Rights (Open Elective - II) | Elective | 3 | Patent Law, Copyright Law, Trademark Law, Design Rights, Trade Secrets |
| 191AI781 | Project Work – Phase I | Project | 6 | Problem Definition, Project Proposal, Literature Survey, System Architecture Design, Preliminary Implementation |
| 191AI791 | Internship (Industry/Research) | Internship | 1 | Industry Exposure, Practical Skill Application, Problem Solving in Real-world, Report Writing, Presentation of Findings |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 191AI926 | GPU Computing for AI (Professional Elective - VI) | Elective | 3 | CUDA Architecture, Parallel Programming, Performance Optimization, Deep Learning Frameworks on GPU, Distributed GPU Training |
| 191OE909 | Digital Marketing (Open Elective - III) | Elective | 3 | Search Engine Optimization (SEO), Search Engine Marketing (SEM), Social Media Marketing, Content Marketing, Email Marketing |
| 191AI881 | Project Work – Phase II | Project | 5 | Detailed Design and Development, Implementation and Testing, Evaluation and Refinement, Comprehensive Project Report, Final Presentation and Demonstration |
