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Muhammad Ali Jinnah University, Karachi (COURSE OUTLINE) Program Semester Course Course Code Prerequisite(s) BS(CS) Spring 2017 Artificial Intelligence CS5803 1. Computer Programming 2. Discrete Mathematics 3. Probability & Statistics Course Instructors Text Book Ahsan Ghazi Russell, Stuart and Peter Norvig, Artificial Intelligence: A Modern Approach (AIMA), 3rd edition, Prentice-Hall, New Jersey, 2010. ISBN 013-604259-7 Course Description This course is a broad graduate level introduction to the field of artificial intelligence (AI). Topics covered will include state-based problem solving, heuristic (informed) search, constraint satisfaction algorithms, game playing algorithms, propositional and first-order logic, logical inference algorithms, representations of uncertainty, optimal decision making, Bayesian networks, and basic principles of machine learning, Pointers to real-world applications in areas such as computer vision, speech recognition, robotics, etc., will be used as appropriate to illustrate various concepts. Course Learning Outcomes: Upon successful completion of this course, the student shall be able to: Demonstrate fundamental understanding of the history of artificial intelligence (AI) and its foundations. Understand different types of AI agents. Know various AI search algorithms (uninformed, informed, heuristic, constraint satisfaction, genetic algorithms). Understand the fundamentals of knowledge representation (logic-based, frame-based, semantic nets), inference and theorem proving. Know how to build simple knowledge-based systems. Demonstrate working knowledge of reasoning in the presence of incomplete and/or uncertain information. Ability to apply knowledge representation, reasoning, and machine learning techniques to real-world problems. Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal implications. Topics Reading Assignments 1 Introduction to Artificial Intelligence AIMA, Chapter 1 2. State Space Search AIMA, Chapter 2& 3 3. Uninformed Search AIMA, Chapter 3 4. Heuristic Search AIMA, Chapter 4 5. Local Search AIMA, Chapter 4 6. Adversarial Search AIMA, Chapter 5 7. 8. Constraint Satisfaction AIMA, Chapter 6 Presentation 9. Midterm Exam 10. First Order Logic AIMA, Chapter 8 11. First Order Inference AIMA, Chapter 9 12. Uncertainty AIMA, Chapter 13 13. Bayesian Networks AIMA, Chapter 14 14. learning AIMA, Chapter 18 15. Learning part2 AIMA, Chapter 18 16. Remaining Topics 17. Project Presentation Final Exam