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ÇAĞ UNIVERSITY FACULTY OF ARTS AND SCIENCES Learning Outcomes of the Course Code Course Title Credit ECTS MAT 457 Artificial Intelligence 3 (2-2) 5 Prerequisites None Language of Instruction Mode of Delivery Face to face Type and Level of Course Elective / 4.Year / Fall Semester Lecturers Name(s) Contacts Lecture Hours Office Hours Course Coordinator Asst.Prof.Dr. Mutlu AVCI [email protected] Course Objective At the end of this course, students should have a good understanding of the research questions and methods used in artificial intelligence, and should also be able to use this knowledge to implement some of these methods. Relationship Students who have completed the course successfully should be able to Prog. Output Net Effect 1 Represent intelligent behavior in terms of agent. 3. 4, 5 4, 5, 4 2 Search a space of answers for a solution to a problem in 4,5, 7 3,4,3 practical time. 3 Represent problems in terms of logic and deduction. 2, 3, 4 3, 3, 3 4 Know logical representations of uncertainty, and rational 3, 4, 5 4, 3, 3 decision making in uncertain environments. Course Description: This course is an introductory survey of artificial intelligence. The course will cover the history, theory, and computational methods of artificial intelligence. Basic concepts include representation of knowledge and computational methods for reasoning. Course Contents:( Weekly Lecture Plan ) Weeks Topics Preparation Teaching Methods 1 Introduction Textbook Ch.1 Lectures and Demonstration 2 Agents Textbook Ch.2 Lectures and Demonstration 3 Systematic search Textbook Ch.3 & 4 Lectures and Demonstration 4 Heuristic and Local search Textbook Ch.3 &4 Lectures and Demonstration 5 Constraint Satisfaction Textbook Ch.3 & 4 Lectures and Demonstration 6 Propositional logic Textbook Ch.7 Lectures and Demonstration 7 Predicate logic Textbook Ch.8 Lectures and Demonstration 8 Classical Planning Textbook Ch.10 Lectures and Demonstration 9 Bayesian Networks and Probability review Textbook Ch.13-14 Lectures and Demonstration 10 Exact inference in Bayesian Networks Textbook Ch.14 Lectures and Demonstration 11 Learning probabilistic models Textbook Ch.20 Lectures and Demonstration 12 Decision trees Textbook Ch.18 Lectures and Demonstration 13 Perceptrons Textbook Ch.18 Lectures and Demonstration 14 Learning theory Textbook Ch.18 Lectures and Demonstration REFERENCES Textbook Russell and Norvig Artificial Intelligence: A Modern Approach 3rd Edition, 2010. Activities Midterm Exam Project Effect of The Midterm Exam Effect of The Final Exam Contents Hours in Classroom Hours out Classroom Projects Midterm Exam Number 1 1 ASSESSMENT METHODS Effect 15% 25% 40% 60% ECTS TABLE Number 14 14 1 1 Notes Hours 4 3 12 12 Total 56 42 12 12 Final Exam 1 27 Total Total / 30 ECTS Credit RECENT PERFORMANCE 27 149 149/30=4.97 5