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Meri Shiksha

B.Tech in Artificial Intelligence and Machine Learning Syllabus

Engineering Eligibility: 10+2 Duration: 4 Yearly Course Mode: Regular

Bachelor of Technology in Artificial Intelligence and Machine Learning Syllabus

Table of Contents

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.