BTech AI & ML Syllabus Structure (4 Years)
The BTech Artificial Intelligence & Machine Learning syllabus is designed to build strong foundations in mathematics,
programming, and computer science, followed by specialized subjects in artificial intelligence, machine learning,
and data-driven technologies.
The curriculum emphasizes theory, laboratory practice, internships, and real-world project work.
Year 1: Engineering & Programming Foundation
The first year focuses on engineering mathematics, basic sciences, and introductory programming skills.
Semester 1
- Engineering Mathematics I: Calculus, matrices, and linear algebra.
- Engineering Physics: Applied physics concepts.
- Basic Electrical Engineering: Fundamentals of electrical systems.
- Programming Fundamentals: Introduction to C / Python.
Semester 2
- Engineering Mathematics II: Probability and statistics.
- Data Structures: Arrays, stacks, queues, and linked lists.
- Engineering Chemistry / Environmental Studies: Sustainability concepts.
- Digital Logic Design: Logic gates and circuits.
Year 2: Core Computer Science Subjects
The second year introduces core computer science concepts required for AI & ML.
Semester 3
- Object-Oriented Programming: Java / Python OOP concepts.
- Database Management Systems: SQL and data modeling.
- Discrete Mathematics: Graph theory and logic.
- Computer Organization: CPU architecture and memory.
Semester 4
- Design & Analysis of Algorithms: Sorting, searching, complexity.
- Operating Systems: Processes, memory, and scheduling.
- Software Engineering: SDLC and project management.
- Probability for Data Science: Statistical modeling.
Year 3: Artificial Intelligence & Machine Learning Core
The third year focuses on artificial intelligence techniques and machine learning algorithms.
Semester 5
- Artificial Intelligence: Search algorithms and reasoning.
- Machine Learning: Supervised and unsupervised learning.
- Data Science: Data preprocessing and visualization.
- Python for AI: NumPy, Pandas, and Matplotlib.
Semester 6
- Deep Learning: Neural networks and CNNs.
- Natural Language Processing: Text analytics and language models.
- Computer Vision: Image processing and recognition.
- Elective I: Specialized AI subject.
Year 4: Advanced AI, Specialization & Industry Exposure
The final year emphasizes advanced AI topics, specialization, internships, and project-based learning.
Semester 7
- Big Data Analytics: Hadoop and Spark fundamentals.
- AI Ethics & Responsible AI: Ethical and legal aspects.
- Elective II & III: Advanced AI / ML specializations.
- Industry Internship: Practical exposure.
Semester 8
- Capstone Project: Real-world AI / ML problem.
- Project Viva Voce: Evaluation and presentation.
- Entrepreneurship / Innovation: Startup fundamentals.
Recommended Books for BTech AI & ML
| Subject |
Book Title |
Author |
| Artificial Intelligence |
Artificial Intelligence: A Modern Approach |
Stuart Russell & Peter Norvig |
| Machine Learning |
Pattern Recognition and Machine Learning |
Christopher Bishop |
| Deep Learning |
Deep Learning |
Ian Goodfellow |
| Data Science |
Python for Data Analysis |
Wes McKinney |
BTech AI & ML Syllabus FAQs
Q1: Is the BTech AI & ML syllabus difficult for average students?
The syllabus is technical but manageable.
Mathematics and programming require practice.
Regular study helps understanding.
Q2: Does the syllabus include hands-on projects and labs?
Yes, labs and projects are core components.
Students work on real datasets.
Practical learning is emphasized.
Q3: Are internships mandatory in BTech AI & ML?
Most universities mandate internships.
Industry exposure improves job readiness.
Internships enhance resumes.
Q4: Does the syllabus cover modern AI tools and frameworks?
Yes, Python-based tools are taught.
ML and DL frameworks are introduced.
Industry relevance is maintained.
Q5: Are electives available for specialization in AI domains?
Yes, electives allow focused learning.
Students can choose advanced AI topics.
Specialization supports career goals.
Q6: Does the syllabus prepare students for research and higher studies?
Yes, core AI and ML concepts are strong.
Suitable for MTech and MS programs.
Research projects support academics.
Q7: Is mathematics heavily used throughout the AI & ML syllabus?
Mathematics is essential for algorithms.
Probability and linear algebra are important.
Concepts are applied practically.
Q8: Does completing the AI & ML syllabus guarantee a job?
No syllabus guarantees a job.
Skills, projects, and internships matter.
Strong profiles get better opportunities.
