Artificial Intelligence Syllabus - BCA (TU)
View and download full syllabus of Artificial Intelligence
Course Description
Course Description
Artificial Intelligence (CACS401) is a core credit course structured under the official university academic syllabus for Bachelor of Computer Application.
Objective
The objective of this course is to introduce the basic principles, techniques, and applications of Artificial Intelligence. Upon the completion students will be able to:
- Equip students with deep theoretical foundations in Artificial Intelligence.
- Develop practical problem-solving, laboratory, and implementation skills.
- Prepare graduates for industry careers, research, and national university examinations.
Learning Outcomes
- Demonstrate rigorous technical knowledge and conceptual mastery of Artificial Intelligence.
- Design, implement, and analyze efficient algorithms and practical frameworks.
- Solve representative theoretical proofs and complex applied problems independently.
Unit Contents
Course Contents
Unit 1: Introduction to Artificial Intelligence[6 Hrs.]
Unit 1:Comprehensive study notes, key principles, and examples for Introduction to Artificial Intelligence.
Unit 2: Intelligent Agents and Environments[6 Hrs.]
Unit 2:Comprehensive study notes, key principles, and examples for Intelligent Agents and Environments.
Unit 3: Problem Solving and State Space Search[9 Hrs.]
Unit 3:Comprehensive study notes, key principles, and examples for Problem Solving and State Space Search.
Unit 4: Adversarial Search and Game Playing[6 Hrs.]
Unit 4:Comprehensive study notes, key principles, and examples for Adversarial Search and Game Playing.
Unit 5: Knowledge Representation and Logic[8 Hrs.]
Unit 5:Comprehensive study notes, key principles, and examples for Knowledge Representation and Logic.
Unit 6: Machine Learning and Artificial Neural Networks[8 Hrs.]
Unit 6:Comprehensive study notes, key principles, and examples for Machine Learning and Artificial Neural Networks.
Unit 7: Expert Systems, NLP, and Computer Vision[7 Hrs.]
Unit 7:Comprehensive study notes, key principles, and examples for Expert Systems, NLP, and Computer Vision.
Teaching Method
Classroom lectures (3 hours/week), practical laboratory assignments (3 hours/week), and project work.
Evaluation Scheme
Internal Assessment (40 Marks: Theory Exam, Practical Exam, Attendance, Assignments) and Final University Board Examination (60 Marks).
Reference Books
- Artificial Intelligence: A Modern Approach by Stuart Russell & Peter Norvig (4th Edition, Pearson)
- Artificial Intelligence by Elaine Rich, Kevin Knight & Shivashankar B. Nair (McGraw Hill)
Official Syllabus Source:View official university document