AI & Machine LearningSemester 4-2 syllabus
Review the course structure, credits and unit-wise topics for fourth year, semester two.
- Regulation
- R23
- Semester
- 4-2
- Coverage
- 1 subject sections
- Credits
- 12
Jump to a subject1 sections
Check this guide against JNTUK documents
This page is a student-friendly study guide. Use the official university documents below as the final authority for course structure, revisions and examination rules.
- Institution
- JNTUK
- Regulation
- R23
- Branch
- AI & ML
- Links checked
The final semester is dedicated entirely to a full-time internship or project work, giving students a capstone opportunity to apply the AI&ML curriculum — from data structures and machine learning through deep learning, NLP, and reinforcement learning — to a substantial real-world or industry-aligned problem. No classroom coursework runs alongside it; the semester’s credit weight is concentrated in supervised project execution and evaluation.
Subjects
Full-Semester Internship / Project Work
- Problem identification and scoping in consultation with an academic or industry supervisor.
- Literature or technology survey relevant to the chosen problem domain.
- System design and iterative implementation, drawing on the programming, ML, and systems foundations built across earlier semesters.
- Testing, evaluation, and refinement of the resulting system or research outcome.
- Documentation of the work and a final viva-voce/project defense.
L-T-P: 0-0-24, 12 credits
Per R23 regulations, students must complete at least one MOOC course (3 of the 160 total programme credits) by this point in the degree if not already fulfilled earlier. Students who opted into the Honors track may also complete their second Honors-pool course here, drawing from options such as Agentic AI or Adversarial Machine Learning introduced in the prior semester.
Semester total: 0-0-24 contact hours, 12 credits — the smallest contact-hour load of the programme, reflecting its fully project-based structure.