Download Introduction to AI

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