B-TECH in Artificial Intelligence Machine Learning at Manipal Academy of Higher Education
Udupi, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at Manipal Academy of Higher Education Udupi?
This Artificial Intelligence and Machine Learning B.Tech program at Manipal Academy of Higher Education focuses on equipping students with advanced theoretical knowledge and practical skills in AI and ML. It is designed to meet the rapidly evolving demands of the Indian tech industry, where AI/ML is pivotal for innovation across sectors. The program distinguishes itself by integrating core computer science foundations with specialized modules in deep learning, NLP, computer vision, and reinforcement learning, ensuring a comprehensive understanding of intelligent systems.
Who Should Apply?
This program is ideal for aspiring engineers and innovators eager to delve into the cutting-edge fields of AI and ML. It attracts fresh graduates with a strong mathematical and programming aptitude seeking entry into data science, AI engineering, or research roles. Working professionals aiming to upskill and leverage AI in their current domains, or career changers transitioning into the high-demand AI industry, will also find this curriculum beneficial due to its rigorous and applied approach.
Why Choose This Course?
Graduates of this program can expect diverse career paths within India''''s thriving AI ecosystem, including roles as AI Engineers, Machine Learning Scientists, Data Scientists, or NLP Specialists. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly higher. The program prepares students for roles in startups, large tech firms, and R&D divisions, fostering a strong foundation for professional certifications and continuous growth in the dynamic field of artificial intelligence.
Student Success Practices
Foundation Stage
Master Programming & Data Structures Fundamentals- (Semester 1-2)
Dedicate significant time to hands-on practice in C and Python, focusing on fundamental programming concepts, object-oriented programming, and various data structures like arrays, linked lists, trees, and graphs. Solve coding problems regularly to build logical thinking and efficient algorithm design.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation
Career Connection
Strong fundamentals are crucial for cracking coding interviews and building efficient AI/ML models. It ensures a solid base for complex algorithm development required in advanced AI.
Build Strong Mathematical Acumen- (Semester 1-2)
Focus on understanding Calculus, Linear Algebra, Probability, and Statistics thoroughly. These are the mathematical pillars of AI/ML. Practice problems from textbooks and online resources to gain intuition behind concepts like gradients, matrix operations, probability distributions, and hypothesis testing.
Tools & Resources
Khan Academy, MIT OpenCourseWare for Mathematics, Introduction to Linear Algebra by Gilbert Strang
Career Connection
A deep mathematical understanding enables comprehension of advanced ML algorithms, helps in model tuning, and is vital for research and development roles in AI.
Engage in Peer Learning & Collaborative Projects- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and collectively solve problems. Work on small, extracurricular projects together to apply learned concepts, even if they are simple programs. Participate in academic discussions and review sessions.
Tools & Resources
GitHub for collaborative coding, Discord/Slack for group discussions, University library resources
Career Connection
Develops teamwork, communication, and problem-solving skills, which are highly valued in industry roles. It also helps clarify doubts and reinforce learning from multiple perspectives.
Intermediate Stage
Develop Practical AI/ML Application Skills- (Semester 3-5)
Go beyond theoretical knowledge by implementing core AI/ML algorithms in Python using libraries like NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch. Work on real-world datasets from platforms like Kaggle to build, train, and evaluate models.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebooks, Coursera/edX courses on ML/DL
Career Connection
This hands-on experience is critical for building a strong portfolio, demonstrating practical expertise to potential employers, and preparing for roles as ML Engineers or Data Scientists.
Explore Industry-Relevant AI/ML Domains- (Semester 4-5)
Identify a specific area within AI/ML, for example, Computer Vision, NLP, or Reinforcement Learning, that interests you. Take online courses, read research papers, and work on projects focused on this niche to gain specialized knowledge and skills that align with industry trends.
Tools & Resources
ArXiv, Towards Data Science (Medium), Specialized MOOCs
Career Connection
Specialization makes you a more attractive candidate for specific roles in companies and helps you stand out in a competitive job market. It showcases depth of knowledge beyond general ML.
Network with Professionals & Participate in Hackathons- (Semester 3-5)
Attend industry meetups, webinars, and conferences related to AI/ML. Connect with professionals on platforms like LinkedIn to gain insights into industry expectations. Actively participate in hackathons and coding competitions to test your skills under pressure and collaborate with diverse teams.
Tools & Resources
LinkedIn, Meetup.com, Eventbrite, Devpost, Major League Hacking (MLH)
Career Connection
Networking can lead to mentorship, internship opportunities, and job referrals. Hackathons provide practical experience, problem-solving exposure, and opportunities to build project prototypes for your resume.
Advanced Stage
Undertake Significant Research/Industry Projects- (Semester 6-8)
Engage in substantial projects, ideally a major capstone project or an industrial internship, where you apply a comprehensive understanding of AI/ML to solve a complex, real-world problem. Focus on problem definition, data acquisition, model selection, implementation, evaluation, and deployment.
Tools & Resources
Advanced ML/DL frameworks, Cloud platforms (AWS, Azure, GCP), Project management tools, Research papers
Career Connection
Demonstrates your ability to work independently or in a team on large-scale problems, mirroring actual industry challenges. It''''s a key component for your portfolio and interview discussions.
Prepare for Placements and Interviews Strategically- (Semester 7-8)
Systematically prepare for technical interviews by practicing data structures, algorithms, and core AI/ML concepts. Work on communication skills for explaining complex ideas simply. Tailor your resume and portfolio to specific job roles and companies you''''re targeting.
Tools & Resources
InterviewBit, LeetCode (premium), Glassdoor for company-specific interview questions, Mock interviews
Career Connection
Direct preparation for securing placements in top tech companies and startups. Mastering interview skills is paramount for translating academic knowledge into a successful career.
Explore Ethical AI and Responsible Development- (Semester 6-8)
Deepen your understanding of ethical considerations in AI, including bias, fairness, transparency, and accountability. Engage in discussions, read papers, and incorporate ethical thinking into your project designs to ensure the responsible development and deployment of AI systems.
Tools & Resources
AI Ethics guidelines from organizations like Google/Microsoft, Articles on responsible AI, Discussions on platforms like Twitter/LinkedIn
Career Connection
Critical for leadership roles and for contributing to sustainable AI development. Companies increasingly value professionals who understand and can navigate the ethical implications of AI technologies.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2 or A Level or IB or American 12th grade examination with Physics, Mathematics and English as compulsory subjects, along with Chemistry or Biotechnology or Biology or Technical Vocational Subject as optional subjects with a minimum of 50% marks in Physics, Mathematics and any one of the optional subjects, put together.
Duration: 4 years / 8 semesters
Credits: 150 Credits
Assessment: Internal: 50% (for theory and project), 100% (for lab/practical), External: 50% (for theory and project), 0% (for lab/practical)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 101 | Calculus and Linear Algebra for Computing | Core | 4 | Functions and Limits, Differentiation Techniques, Integration Methods, Matrices and Determinants, Vector Spaces and Linear Transformations |
| PYC 101 | Engineering Physics | Core | 4 | Wave Optics, Quantum Mechanics Principles, Solid State Physics, Lasers and Fiber Optics, Electromagnetism Basics |
| CSC 101 | Programming in C | Core | 4 | C Language Fundamentals, Control Structures and Loops, Functions and Recursion, Arrays, Pointers, and Strings, Structures, Unions, and File Handling |
| MED 101 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, CAD Basics |
| HTC 101 | Professional Communication | Core | 2 | Grammar and Vocabulary, Writing Skills for Reports, Oral Communication and Presentations, Interpersonal Skills, Interview Preparation |
| CSL 101 | Introduction to Computing Laboratory | Lab | 1 | C Programming Exercises, Debugging Techniques, Algorithm Implementation, Problem Solving with C |
| PYL 101 | Engineering Physics Laboratory | Lab | 1 | Optics Experiments, Electricity and Magnetism Experiments, Semiconductor Device Characteristics, Measurement Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 102 | Differential Equations and Numerical Methods | Core | 4 | First Order Differential Equations, Higher Order Differential Equations, Laplace Transforms, Numerical Integration, Interpolation Techniques |
| CST 101 | Computer System Essentials | Core | 4 | Digital Logic Gates, Boolean Algebra, Combinational and Sequential Circuits, Computer Organization Basics, Memory Hierarchy and I/O |
| CSC 102 | Data Structures | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Algorithms, Sorting and Searching Techniques |
| EEC 101 | Basic Electrical and Electronics Engineering | Core | 4 | DC and AC Circuits, Transformers and Motors, Diodes and Rectifiers, Transistors and Amplifiers, Operational Amplifiers |
| ENC 101 | Environmental Studies | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Climate Change Impacts, Sustainable Development |
| CSL 102 | Data Structures Laboratory | Lab | 1 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms Implementation, Sorting and Searching Exercises |
| EEL 101 | Basic Electrical and Electronics Engineering Laboratory | Lab | 1 | Basic Electrical Circuit Experiments, Diode and Transistor Characteristics, Op-Amp Applications, Circuit Simulation Tools |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 201 | Discrete Mathematics | Core | 4 | Mathematical Logic, Set Theory and Relations, Functions and Counting, Graph Theory, Algebraic Structures |
| CSC 201 | Object Oriented Programming | Core | 4 | OOP Concepts: Encapsulation, Inheritance, Polymorphism, Classes and Objects, Abstract Classes and Interfaces, Exception Handling, File I/O and Collections |
| CST 201 | Database Management Systems | Core | 4 | Relational Model and SQL, ER Diagrams and Schema Design, Normalization Techniques, Transaction Management, Concurrency Control and Recovery |
| CSC 202 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis and Complexity, Divide and Conquer Algorithms, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| CSE 201 | Web Technologies | Core | 2 | HTML and CSS Fundamentals, JavaScript Basics, DOM Manipulation, Web Servers and HTTP Protocol, Client-Server Architecture |
| CSL 201 | Object Oriented Programming Laboratory | Lab | 1 | OOP Implementation in Java/C++, Inheritance and Polymorphism Examples, Exception Handling Practice, GUI Programming Basics |
| CSL 202 | Database Management Systems Laboratory | Lab | 1 | SQL Queries and Joins, Database Design Practice, Stored Procedures and Triggers, Database Connectivity |
| CSL 203 | Web Technologies Laboratory | Lab | 1 | HTML/CSS Page Design, Interactive JavaScript Elements, Responsive Web Design, Frontend Framework Basics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 202 | Probability and Statistics for AI/ML | Core | 4 | Probability Theory and Axioms, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Regression and Correlation Analysis |
| CST 202 | Operating Systems | Core | 4 | Process Management and Scheduling, Memory Management, File Systems, I/O Management, Deadlocks and Concurrency Control |
| AIM 201 | Artificial Intelligence and Machine Learning Fundamentals | Core | 4 | Introduction to AI and ML, Problem Solving and Search Algorithms, Supervised Learning: Regression and Classification, Unsupervised Learning: Clustering, Model Evaluation and Validation |
| CSE 202 | Computer Networks | Core | 4 | Network Topologies and Layers, OSI and TCP/IP Models, Data Link Layer Protocols, Routing and Congestion Control, Network Security Fundamentals |
| HSS 201 | Humanities and Social Sciences Elective | Elective | 2 | |
| CSL 204 | Operating Systems Laboratory | Lab | 1 | Linux Commands and Shell Scripting, Process and Thread Management, Inter-Process Communication, Memory Allocation Techniques |
| AIML 201 | Artificial Intelligence and Machine Learning Fundamentals Laboratory | Lab | 1 | Implementation of Supervised Learning Algorithms, Unsupervised Learning Techniques, Data Preprocessing and Feature Engineering, Model Evaluation Metrics, Python for ML Libraries |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CST 301 | Compiler Design | Core | 4 | Lexical Analysis and Finite Automata, Syntax Analysis and Parsing, Semantic Analysis, Intermediate Code Generation, Code Optimization and Generation |
| AIM 301 | Deep Learning | Core | 4 | Artificial Neural Networks, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Optimization Techniques for Deep Learning |
| AIM 302 | Natural Language Processing | Core | 4 | Text Preprocessing and Tokenization, Language Models and N-grams, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation Fundamentals |
| PEC 1 | Professional Elective 1 | Elective | 3 | |
| OEC 1 | Open Elective 1 | Elective | 3 | |
| AIML 301 | Deep Learning Laboratory | Lab | 1 | Implementation of CNNs and RNNs, Working with TensorFlow/PyTorch, Image Classification Tasks, Sequence Prediction Models |
| AIML 302 | Natural Language Processing Laboratory | Lab | 1 | Text Preprocessing using NLTK, Building Language Models, Sentiment Analysis Implementation, Word Embeddings Techniques |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CST 302 | Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing Strategies, Project Management and Quality Assurance |
| AIM 303 | Reinforcement Learning | Core | 4 | Markov Decision Processes (MDPs), Value and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Exploration vs. Exploitation |
| AIM 304 | Computer Vision | Core | 4 | Image Filtering and Enhancement, Feature Detection and Extraction, Object Recognition and Detection, Image Segmentation, Facial Recognition Systems |
| PEC 2 | Professional Elective 2 | Elective | 3 | |
| OEC 2 | Open Elective 2 | Elective | 3 | |
| AIML 303 | Reinforcement Learning Laboratory | Lab | 1 | Implementation of MDPs, Q-Learning Agents, Policy Gradient Methods, OpenAI Gym Environments |
| AIML 304 | Computer Vision Laboratory | Lab | 1 | Image Processing with OpenCV, Object Detection using Pre-trained Models, Image Segmentation Techniques, Feature Matching Applications |
| AIP 301 | Minor Project | Project | 1 | Problem Identification, Literature Survey, System Design and Implementation, Testing and Evaluation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PEC 3 | Professional Elective 3 | Elective | 3 | |
| PEC 4 | Professional Elective 4 | Elective | 3 | |
| OEC 3 | Open Elective 3 | Elective | 3 | |
| AIP 401 | Major Project Part I | Project | 6 | Detailed Problem Statement, Comprehensive Literature Review, System Architecture Design, Methodology Planning, Initial Implementation |
| AIMI 401 | Industrial Internship | Project | 3 | Industry Exposure, Application of AI/ML Skills in Real-world Projects, Professional Work Ethics, Technical Report Writing, Mentorship and Feedback |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PEC 5 | Professional Elective 5 | Elective | 3 | |
| AIP 402 | Major Project Part II | Project | 9 | Advanced Implementation and Coding, Extensive Testing and Debugging, Performance Evaluation and Optimization, Final Project Documentation and Presentation, Research Paper/Thesis Writing |