Artificial Intelligence
A complete self-paced learning portal covering search, knowledge representation, machine learning and expert systems — built for the JNTUH R23 curriculum, Department of Computer Science & Engineering.
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Track completion across every unit, quiz and lab experiment. Progress is saved automatically in this browser.
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Course Syllabus
Five units spanning classical search, knowledge representation, machine learning and expert systems — based on Rich & Knight, Artificial Intelligence.
Download Full Syllabus (PDF)AI Lab — 10 Experiments
Each experiment includes aim, algorithm, flowchart, runnable Python code, sample output and explanation.
Assignments
Unit-wise assignments to reinforce concepts. Submit as handwritten scan or typed PDF via your faculty's designated drop link.
MCQ Practice Quizzes
Choose a unit to attempt its quiz. Answers and explanations are revealed instantly.
Question Bank
Long-answer and short-answer questions grouped by unit, aligned to JNTUH exam pattern (2-mark & 10-mark).
Downloads
All course material in one place — slides, notes, programs, question banks, textbooks and lab manuals.
Previous Question Papers
Mid & semester-end papers from previous academic years, JNTUH R23 & equivalent regulations.
| Exam | Regulation | Year | Type | Download |
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Uppala Vijay Kumar
Professor & Course Coordinator
Ph.D. (Machine Learning), M.Tech (CSE)
18+ years of teaching & research experience
Qualifications & Experience
Ph.D., Machine Learning
Specialization in probabilistic reasoning and knowledge-based systems.
M.Tech, Computer Science & Engineering
Gold medalist; thesis on heuristic search optimisation.
18+ Years Teaching Experience
Artificial Intelligence, Machine Learning, Data Structures & Expert Systems.
30+ Publications
Peer-reviewed journals and international conferences in AI/ML.
Research Interests
Research & Publications
Ongoing research directions within the AI & ML research group of the department.
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