B.Tech - (CSE) Artificial Intelligence and Machine Learning
Course Overview
This specialisation integrates core computer science with advanced subjects like machine learning, neural networks, NLP, deep learning, and predictive analytics. Students work with real datasets and tools used in AI-driven industries.
Course Objectives
- To develop strong foundations in AI, ML, and data science.
- To train students to design intelligent systems and algorithms.
- To provide hands-on exposure through datasets, labs, and hackathons.
- To prepare graduates for roles in emerging AI-driven industries.
Learning Outcomes
- Build and train ML and deep learning models.
- Analyze data to extract patterns and generate insights.
- Use cloud platforms and big data tools for real-time implementation.
- Innovate using AI-ML techniques to solve complex challenges.
Curriculum
Semester 1
Practical
- Chemistry Lab
- Python Programming Lab
Theory
- Fundamentals of Technical Communication
- Matrices and Calculus
- Applied Chemistry
- Foundations of Engineering
- Python Programming
- Engineering Graphics
Semester 2
Practical
- Physics Lab
- Digital Electronics Lab
- Data Structures and Algorithms Lab
- Workshop Practices Lab
Theory
- Professional Communication Skills
- Analytical Mathematics
- Physics for Computer Science
- Digital Electronics
- Data Structures and Algorithms
- Foundation Course in Quantitative & Logical Aptitude
Semester 3
Practical
- Operating Systems Lab
- Advanced Programming Practice Lab
- Database Management Systems Lab
- Advanced Corporate Communication Lab
Theory
- Discrete Mathematics
- Object Oriented Design and Programming
- Operating Systems
- Advanced Programming Practice
- Database Management Systems
- Digital Logic and Computer Organization
- Intermediate Quantitative Aptitude and Verbal Skills
Semester 4
Practical
- Design and Analysis of Algorithms Lab
- Artificial Intelligence Lab
- Computer Networks Lab
- Technical Writing and Documentation Lab
Theory
- Probability and Statistics
- Design and Analysis of Algorithms
- Artificial Intelligence
- Formal Language and Automata
- Computer Networks
- Advanced Aptitude and Logical Reasoning
Semester 5
Practical
- Software Engineering and Project Management Lab
- Compiler Design Lab
- Machine Learning Lab
- Professional Communication Practice Lab
Theory
- Software Engineering and Project Management
- Compiler Design
- Machine Learning
- Open Elective I
- Professional Elective I
- Professional Elective II
- Company-Specific Aptitude and Coding Preparation
Semester 6
Practical
- Data Science Lab
- Deep Learning Techniques Lab
- Internship
Theory
- Data Science
- Deep Learning Techniques
- Open Elective II
- Professional Elective III
- Professional Elective IV
- Company-Specific Aptitude and Reasoning Practice
Semester 7
Practical
- Natural Language Processing Lab
- Computer Vision Lab
- Project Phase I
Theory
- Artificial Neural Networks
- Natural Language Processing
- Computer Vision
- Open Elective III
- Open Elective IV
- Professional Elective V
Semester 8
Project
- Project Phase II
Open Electives
- Web Programming
- Cloud Computing Fundamentals
- Mobile Application Development
- Data Analytics
Professional Electives
- Programming in Java
- Digital Image Processing
- Genetic Algorithm and its Applications
- Robot Programming
- Software Engineering in Artificial Intelligence
- Accelerated Data Science
- Data Mining and Analytics
- Advanced Algorithms
- Marketing Analytics
- Computational Neuroscience
- Nature Inspired Computing Techniques
- Information Retrieval
- Design Principles of Smart Space Management
- Business Intelligence and Analytics
- Pattern Recognition Techniques
- Artificial Intelligence in Genomics and Disease Prediction
- Machine Learning in Drug Discovery
- IoT Concepts and Applications
- Fuzzy Logic and its Applications
- Robotics: Computational Motion Planning
- Reinforcement Learning Techniques
- Cyber Physical Systems
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