B.Tech in Artificial Intelligence and Machine Learning Syllabus
Updated on :
April 18, 2025 11:46 AM
Reviewed By:
Cheif Reviewer
The curriculum of B.Tech in Artificial Intelligence and Machine Learning is meticulously designed to provide students with a comprehensive understanding of foundational principles, advanced algorithms, and emerging technologies shaping the field. Spanning eight semesters, the syllabus encompasses a diverse range of subjects tailored to equip students with the requisite skills and knowledge to excel in the field of AI and ML.
B.Tech in AI and ML Syllabus
Below are the semester-wise details of the B.Tech in AI and ML Syllabus:
Semester 1 | Semester 2 |
Mathematics I | Mathematics II |
Physics | Basic Electronics Engineering |
Physics Lab | Basic Electronics Engineering Lab |
Programming in C Language | Data Structures with C |
Programming in C Language Lab | Data Structures-Lab |
Playing with Big Data | Discrete Mathematical Structures |
Open Source and Open Standards | Introduction to IT and Cloud Infrastructure Landscape |
Communication WKSP 1.1 | Communication WKSP 1.2 |
Communication WKSP 1.1 Lab | Communication WKSP 1.2 Lab |
Seminal Events in Global History | Environmental Studies |
Appreciating Art Fundamentals |
Semester 3 | Semester 4 |
Computer System Architecture | Operating Systems |
Design and Analysis of Algorithms | Data Communication and Computer Networks |
Design and Analysis of Algorithms Lab | Data Communication and Computer Networks Lab |
Web Technologies | Introduction to Java and OOPS |
Web Technologies Lab | Introduction to Java and OOPS Labs |
Functional Programming in Python | Applied Statistical Analysis (for AI and ML) |
Introduction to Internet of Things | Current Topics in AI and ML |
Communication WKSP 2.0 | Database Management Systems & Data Modelling |
Communication WKSP 2.0 Lab | Database Management Systems & Data Modelling Lab |
Securing Digital Assets | Impact of Media on Society |
Introduction to Applied Psychology |
Semester 5 | Semester 6 |
Formal Languages & Automata Theory | Introduction to Machine Learning |
Mobile Application Development | Natural Language Processing |
Mobile Application Development Lab | Minor Subject 2 – General Management |
Algorithms for Intelligent Systems | Minor Subject 3 - Finance for Modern Professional |
Current Topics in AI and ML | Design Thinking |
Software Engineering & Product Management | Communication WKSP 3.0 |
Minor Subject: - 1. Aspects of Modern English Literature or Introduction to Linguistics | Minor Project II |
Minor Project I |
Semester 7 | Semester 8 |
Program Elective | Web Technologies |
Major Project-1 | Major Projects 2 |
Comprehensive Examination | Program Elective-5 |
Professional Ethics and Values | Program Elective-6 |
Industrial Internship | Open Elective - 4 |
Open Elective - 3 | Universal Human Value & Ethics |
CTS-5 Campus to Corporate | Robotics and Intelligent Systems |
Introduction to Deep Learning |
BTech AI and ML Subjects
The Bachelor of Technology in Artificial Intelligence and Machine Learning program’s curriculum has provided graduate candidates with a robust foundation in both theoretical and practical aspects necessary for knowledge and employability enhancement in this era. The following is a condensed overview of the subjects addressed in the program, arranged according to the patterns of the respective university’s syllabi.
BTech AI and ML Syllabus in Bikaner Technical University
Year 1 Subjects
Semester 1 | Semester 2 |
Linear Algebra and Differential Calculus | Digital Logic Design |
Applied Physics | Object-Oriented Programming in C++ |
English | Probability and Statistics |
Programming for Problem-Solving | Discrete Mathematics |
Engineering Graphics | Database Management Systems |
Differential Equations and Vector Calculus | Computer Organization |
Engineering Chemistry | Operating Systems |
Data Structures | Economics and Accounting for Engineers |
| Java Programming |
| Design and Analysis of Algorithms |
| Lab Work |
Year 2 Subjects
Semester 3 | Semester 4 |
Computer Networks | Cryptography and Network Security |
Data Warehousing and Data Mining | Neural Networks and Deep Learning |
Artificial Intelligence | Fundamentals of Management and Entrepreneurship |
Machine Learning | Lab Work |
Big Data Analytics | |
Software Engineering |
BTech AI and ML Syllabus in JNTU
Below are the details of the BTech AI and ML Syllabus at JNTU:
Semester 1 | Semester 2 |
Mathematics - I | Mathematics - II |
Chemistry | Applied Physics |
Basic Electrical Engineering | Programming for Problem Solving |
Engineering Workshop | Engineering Graphics |
English | Applied Physics Lab |
Engineering Chemistry Lab | Programming for Problem-Solving Lab |
English Language and Communication Skills Lab | Environmental Science |
Basic Electrical Engineering Lab |
Semester 3 | Semester 4 |
Discrete Mathematics | Formal Language and Automata Theory |
Data Structures | Software Engineering |
Mathematical and Statistical Foundations | Operating Systems |
Computer Organization and Architecture | Database Management Systems |
Python Programming | Object-Oriented Programming using Java |
Business Economics & Financial Analysis | Operating Systems Lab |
Data Structures Lab | Database Management Systems Lab |
Python Programming Lab | Java Programming Lab |
Gender Sensitization Lab | Constitution of India |
Semester 5 | Semester 6 |
Design and Analysis of Algorithms | Artificial Intelligence |
Machine Learning | DevOps |
Computer Networks | Natural Language Processing |
Compiler Design | Professional Elective – III |
Professional Elective - I | Artificial Intelligence and Natural Language Processing Lab |
Professional Elective - II | DevOps Lab |
Machine Learning Lab | Professional Elective - III Lab |
Computer Networks Lab | Environmental Science |
Advanced Communication Skills Lab |
Semester 7 | Semester 8 |
Neural Networks & Deep Learning | Organizational Behaviour |
Reinforcement Learning | Professional Elective - VI |
Professional Elective - IV | Open Elective - III |
Professional Elective - V | Project Stage - II |
Open Elective - II | |
Deep Learning Lab | |
Industrial-Oriented Mini Project/ Summer Internship | |
Seminar | |
Project Stage |
BTech AI and ML Syllabus: Teaching Methodology and Techniques
The BTech AI and ML program that is delivered to Manipal Institute of Technology students is structured in a way that teaches comprehensive theory and application methodology regarding AI and ML. The curriculum includes:
- Case Studies: Analysing real-world scenarios to understand the application of AI and ML principles.
- Experiments: Hands-on experiments to reinforce theoretical knowledge and develop practical skills.
- Real-time Projects: Engaging in projects that simulate real-world challenges, enhancing problem-solving abilities.
- Internships: Providing opportunities for students to gain industry exposure and practical experience.
- Practical Sessions: Conducting practical sessions to apply theoretical concepts and develop technical proficiency.
BTech AI and ML Books
For a student to do well in the Bachelor of Technology program in Artificial Intelligence and Machine Learning, they need resources that cover the foundation principles and more complex concepts. The following books are thus recommended to be fundamental references for a BTech AI and ML student:
Books | Author |
Discrete Mathematics and its Applications with Combinatorics and Graph Theory | Kenneth H Rosen, 7th Edition, TMH |
Discrete Mathematics | Richard Johnsonbaugh, 7th Edn., Pearson Education |
Fundamentals of Data Structures in C | E. Horowitz, S. Sahni and Susan Anderson Freed, Universities Press |
Computer System Architecture | M. Moris Mano, Third Edition, Pearson/PHI |
Core Python Programming | Wesley J. Chun, Second Edition, Pearson |
Software Engineering, A Practitioner’s Approach | Roger S. Pressman, 6th Edition, McGraw Hill International Edition |
Advanced Programming in the UNIX Environment | W.R. Stevens, Pearson Education |
Database System Concepts | Silberschatz, Korth, McGraw Hill, 5th Edition |
