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B-TECH in Ai Ml at Sri Ramachandra Institute of Higher Education and Research
Chennai, Tamil Nadu
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About the Specialization
What is AI & ML at Sri Ramachandra Institute of Higher Education and Research Chennai?
This Artificial Intelligence & Machine Learning program at Sri Ramachandra Institute of Higher Education and Research focuses on equipping students with advanced theoretical knowledge and practical skills in cutting-edge AI technologies. It addresses the growing demand for AI professionals in India, emphasizing real-world applications and innovative problem-solving crucial for driving technological advancements across various sectors. The program aims to foster analytical thinking and hands-on expertise.
Who Should Apply?
This program is ideal for ambitious fresh graduates seeking entry into the rapidly expanding AI/ML industry, and for working professionals looking to upskill in specialized areas like deep learning or natural language processing. It also caters to career changers from conventional IT roles transitioning into high-demand AI fields. Candidates typically possess a strong aptitude for mathematics, programming, and logical reasoning.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as AI Engineers, Machine Learning Scientists, Data Scientists, Deep Learning Specialists, and AI Consultants. Entry-level salaries typically range from INR 5-8 LPA, growing significantly with experience. The program aligns with industry needs, potentially leading to roles in product development, research, and data-driven decision-making within Indian tech giants and innovative startups.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time to mastering C/C++ and Python. Focus on problem-solving logic, data structures, and algorithms. Regularly practice coding challenges to build a strong base. Engage with peer learning groups to discuss approaches and solutions.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, competitive programming communities
Career Connection
A strong programming foundation is essential for almost all technical roles in AI/ML, directly impacting performance in technical interviews and project development.
Build Strong Mathematical Acumen- (Semester 1-2)
Focus on understanding the core concepts of Linear Algebra, Calculus, Probability, and Statistics. These are the building blocks for most AI/ML algorithms. Utilize online courses or textbooks to supplement classroom learning and practice problem-solving.
Tools & Resources
Khan Academy, MIT OpenCourseware (Mathematics), 3Blue1Brown YouTube channel, standard textbooks like ''''Probability and Statistics for Engineers''''
Career Connection
A solid mathematical background is crucial for comprehending, designing, and optimizing complex AI/ML models, which is highly valued by research and development roles.
Develop Effective Study Habits & Networking- (Semester 1-2)
Cultivate disciplined study routines, time management, and note-taking skills. Actively participate in academic discussions and form study groups with peers. Attend department orientation programs and connect with senior students for mentorship and guidance.
Tools & Resources
Notion, Evernote, Google Calendar for planning, LinkedIn for initial professional networking
Career Connection
Good academic habits ensure a strong GPA, while early networking can provide insights into career paths and future opportunities.
Intermediate Stage
Hands-on AI/ML Project Development- (Semester 3-5)
Go beyond theoretical understanding by undertaking small AI/ML projects. Start with guided tutorials and gradually build independent projects using real-world datasets. Focus on applying learned algorithms and exploring different libraries.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebooks, Scikit-learn, TensorFlow, PyTorch, GitHub
Career Connection
Practical project experience is critical for demonstrating skills to employers and is a cornerstone of any AI/ML portfolio. It makes candidates stand out during placements.
Participate in Hackathons and Competitions- (Semester 3-5)
Actively seek out and participate in AI/ML-focused hackathons, coding competitions, and data science challenges. These platforms offer opportunities to work on diverse problems, learn new tools, and collaborate with peers under pressure.
Tools & Resources
Major League Hacking (MLH), Devpost, Analytics Vidhya, College-level hackathon announcements
Career Connection
Participation showcases problem-solving skills, teamwork, and quick learning abilities, which are highly regarded by recruiters. Winning or strong performance can lead to internship offers.
Seek Internships and Industry Exposure- (Semester 4-5)
Actively search for and apply to internships related to AI/ML during summer breaks. Even short-term internships provide invaluable exposure to industry practices, tools, and real-world challenges, helping to bridge the gap between academia and industry.
Tools & Resources
Internshala, LinkedIn Jobs, college placement cell, company career pages
Career Connection
Internships are often direct pathways to full-time employment and provide practical experience that makes a resume much more attractive to potential employers.
Advanced Stage
Specialize through Advanced Projects & Research- (Semester 6-8)
Identify a specific area within AI/ML (e.g., Computer Vision, NLP, Reinforcement Learning) and pursue advanced projects, dissertations, or research papers. Aim to contribute to open-source projects or publish in student conferences.
Tools & Resources
arXiv, Google Scholar, specific research group websites, GitHub for open-source contributions
Career Connection
Specialization demonstrates deep expertise and passion, making you a strong candidate for niche roles, R&D positions, or higher studies.
Ace Placement Preparation & Mock Interviews- (Semester 7-8)
Begin intensive preparation for campus placements, focusing on technical aptitude, coding, and behavioral skills. Practice mock interviews with peers, mentors, and career counselors to refine communication and problem-solving under pressure.
Tools & Resources
InterviewBit, AlgoExpert, Glassdoor, LinkedIn for company-specific interview experiences, college placement cell workshops
Career Connection
Thorough preparation significantly increases the chances of securing desirable placements with top companies, ensuring a strong start to your career.
Build a Professional Portfolio & Network- (Semester 7-8)
Compile all significant projects, research work, and certifications into a well-structured online portfolio (e.g., personal website, GitHub profile). Actively network with industry professionals, alumni, and faculty to explore job market trends and opportunities.
Tools & Resources
Personal website builders (e.g., GitHub Pages, Google Sites), LinkedIn, professional conferences and workshops
Career Connection
A strong portfolio serves as a tangible demonstration of your skills, while networking can open doors to unadvertised positions and mentorship, accelerating career growth.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2 / HSC with Physics, Chemistry, and Mathematics (or equivalent subject) as compulsory subjects, with a minimum aggregate percentage (typically 50-60%) as per institutional norms.
Duration: 4 years (8 semesters)
Credits: 168 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS101 | Professional English | Core | 2 | Technical Communication, Written and Verbal Skills, Report Writing, Presentation Skills, Soft Skills |
| MA101 | Engineering Mathematics - I | Core | 4 | Differential Calculus, Integral Calculus, Matrices, Vector Calculus, Ordinary Differential Equations |
| PH101 | Engineering Physics | Core | 3 | Quantum Physics, Solid State Physics, Fiber Optics and Lasers, Wave Optics, Applied Acoustics |
| CS101 | Programming for Problem Solving | Core | 3 | C Programming Basics, Control Structures, Functions and Arrays, Pointers and Structures, File Handling |
| GE101 | Engineering Graphics | Core | 2 | Projections of Points and Lines, Projections of Solids, Section of Solids, Isometric Projections, Orthographic Projections |
| PH102 | Engineering Physics Lab | Lab | 1 | Optical Instruments, Semiconductor Devices, Elasticity Experiments, Thermal Conductivity, Spectrometer Applications |
| CS102 | Programming for Problem Solving Lab | Lab | 1 | C Program Implementation, Conditional Statements, Looping Constructs, Function Calls, Array and String Operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA102 | Engineering Mathematics - II | Core | 4 | Multiple Integrals, Vector Calculus Applications, Complex Numbers, Laplace Transforms, Fourier Series |
| CY101 | Engineering Chemistry | Core | 3 | Water Technology, Corrosion and its Control, Electrochemistry, Fuels and Combustion, Polymers and Composites |
| EE101 | Basic Electrical and Electronics Engineering | Core | 3 | DC and AC Circuits, Electrical Machines, Diodes and Transistors, Operational Amplifiers, Digital Logic Gates |
| CS103 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| ME101 | Engineering Mechanics | Core | 3 | Statics of Particles, Equilibrium of Rigid Bodies, Friction, Dynamics of Particles, Work and Energy Principles |
| CY102 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Water Hardness Determination, pH and Conductivity Measurement, Spectrophotometry, Corrosion Studies |
| CS104 | Data Structures Lab | Lab | 1 | Linked List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Representation, Sorting and Searching Practice |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Probability and Statistics for AI | Core | 4 | Probability Distributions, Bayes'''' Theorem, Hypothesis Testing, Regression Analysis, Stochastic Processes |
| CS201 | Digital Logic and Computer Architecture | Core | 3 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Processor Design, Memory Hierarchy |
| CS202 | Object-Oriented Programming using Python | Core | 3 | OOP Concepts in Python, Classes and Objects, Inheritance and Polymorphism, Exception Handling, File I/O in Python |
| AI201 | Introduction to AI and Machine Learning | Core | 3 | Foundations of AI, Intelligent Agents, Search Algorithms, Supervised Learning Basics, Unsupervised Learning Basics |
| CS203 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, ER Diagrams, Normalization, Transaction Management |
| CS204 | Object-Oriented Programming Lab | Lab | 1 | Python OOP Implementations, Class and Object Creation, Inheritance Examples, Polymorphism Usage, Exception Handling Practice |
| CS205 | Database Management Systems Lab | Lab | 1 | DDL and DML Commands, SQL Joins and Subqueries, Views and Stored Procedures, Database Design Practice, Data Manipulation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA202 | Discrete Mathematics | Core | 4 | Set Theory, Mathematical Logic, Relations and Functions, Graph Theory, Combinatorics |
| CS206 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems |
| AI202 | Machine Learning I | Core | 3 | Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees and Random Forests, Model Evaluation Metrics |
| AI203 | Artificial Neural Networks | Core | 3 | Perceptrons, Multi-layer Perceptrons, Backpropagation Algorithm, Activation Functions, Feedforward Networks |
| CS207 | Design and Analysis of Algorithms | Core | 3 | Algorithm Complexity Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| CS208 | Operating Systems Lab | Lab | 1 | Shell Programming, Process Creation and Management, CPU Scheduling Algorithms, Deadlock Detection, Memory Allocation |
| AI204 | Machine Learning Lab | Lab | 1 | Scikit-learn Implementation, Data Preprocessing, Regression Models, Classification Models, Model Evaluation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Computer Networks | Core | 3 | OSI and TCP/IP Models, Network Topologies, Routing Protocols, Transport Layer Protocols, Network Security Basics |
| AI301 | Deep Learning | Core | 3 | Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs and GRUs, Generative Adversarial Networks (GANs), Transfer Learning |
| AI302 | Natural Language Processing | Core | 3 | Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis |
| AI303 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Q-Learning, SARSA Algorithm, Policy Gradient Methods, Deep Reinforcement Learning |
| PE301 | Professional Elective I (e.g., Computer Vision) | Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition |
| AI304 | Deep Learning Lab | Lab | 1 | TensorFlow/PyTorch Implementation, CNN Model Training, RNN for Sequence Data, Transfer Learning Application, Hyperparameter Tuning |
| AI305 | Natural Language Processing Lab | Lab | 1 | NLTK and SpaCy, Text Classification, Topic Modeling, Text Generation, Chatbot Development |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS302 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization |
| AI306 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Stream Processing |
| AI307 | Cloud Computing for AI | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization, AWS/Azure/GCP AI Services, Containerization (Docker, Kubernetes), Serverless Computing |
| AI308 | Ethical AI and Explainable AI | Core | 3 | AI Ethics Principles, Bias and Fairness in AI, Transparency and Accountability, Interpretability Methods (LIME, SHAP), Privacy Concerns in AI |
| PE302 | Professional Elective II (e.g., Robotics and AI) | Elective | 3 | Robot Kinematics, Robot Vision, Motion Planning, Human-Robot Interaction, AI in Autonomous Systems |
| AI309 | Mini Project | Project | 2 | Problem Formulation, System Design, Implementation and Testing, Report Writing, Presentation Skills |
| AI310 | Big Data & Cloud Lab | Lab | 1 | Hadoop File Operations, MapReduce Programming, Spark Data Processing, AWS/Azure/GCP Cloud Services, Container Deployment |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI401 | AI for Business & Healthcare | Core | 3 | AI in Finance, AI in Marketing, Medical Image Analysis, Drug Discovery with AI, Personalized Medicine |
| PE401 | Professional Elective III (e.g., Internet of Things for AI) | Elective | 3 | IoT Architecture, Sensor Networks, Data Collection from IoT Devices, Edge AI, IoT Security |
| PE402 | Professional Elective IV (e.g., Data Visualization) | Elective | 3 | Principles of Data Visualization, Data Storytelling, Interactive Dashboards, Tools (Tableau, PowerBI), Statistical Graphics |
| OE401 | Open Elective I (e.g., Human Rights) | Elective | 3 | Concept of Human Rights, Universal Declaration of Human Rights, Human Rights in India, Challenges and Issues, Role of Institutions |
| AI402 | Project Work – Phase I | Project | 6 | Literature Review, Problem Definition, Methodology Design, Preliminary Implementation, Mid-term Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| GE401 | Professional Ethics and IPR | Core | 3 | Engineering Ethics, Moral Dilemmas, Intellectual Property Rights (IPR), Patents and Copyrights, Cyber Law and Ethics |
| GE402 | Entrepreneurship and Innovation | Core | 3 | Startup Ecosystem, Business Model Canvas, Market Analysis, Funding Strategies, Innovation Management |
| AI403 | Project Work – Phase II | Project | 12 | Advanced Implementation, Experimentation and Evaluation, Results Analysis, Thesis Writing, Final Defense and Presentation |
