Information Package / Course Catalogue
Artificial Intelligence
Course Code: CSE419
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

The goal of this course is to provide students with a survey of different aspects of Artificial Intelligence (AI).

Course Content

This course provides an introduction to Artificial Intelligence (AI). In this course we will study a number of theories, mathematical formalisms, and algorithms that capture some of the core elements of computational intelligence. We will cover some of the following topics: search, logical representations and reasoning, automated planning, representing and reasoning with uncertainty, decision making under uncertainty, and learning.

Name of Lecturer(s)
Assoc. Prof. Fatih SOYGAZİ
Learning Outcomes
1.1) Be able to develop a variety of approaches with general applicability.
2.2) Be able to understand AI search models and generic search strategies.
3.3) By using Bayesian networks, be able to use the probability as a mechanism for handling uncertainty in AI.
4.4) Be able to explore the design of AI systems that use learning to improve their performance on a given task.
5.5) Be able to present logic as a formalism for representing knowledge in AI systems.
6.6) Be able to address specific domains such as computer vision, natural language processing, and robotics.
Recommended or Required Reading
1.Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114.
Weekly Detailed Course Contents
Week 1 - Theoretical
AI definition, history, and terms. Taxonomic overview of models and features of intelligent agents and their environments.
Week 2 - Theoretical
Knowledge, inference, and logic. Exploration versus goal based systems.
Week 3 - Theoretical
Propositional logic, forward and backward chaining for resolution.
Week 4 - Theoretical
Using Prolog for simple logical programming.
Week 5 - Theoretical
Solving problems by searching.
Week 6 - Theoretical
Informed search and heuristics.
Week 7 - Theoretical
Local search, exploration, and genetic algorithms. Constraint satisfaction problems.
Week 8 - Theoretical
First-order logic and using Prolog for problem solving. Planning problems.
Week 9 - Theoretical
Intelligent agents in physical world: summary of robotics, perception, and natural language processing issues.
Week 10 - Theoretical
Learning, decision nets and Bayesian networks.
Week 11 - Theoretical
Multi-agent systems.
Week 12 - Theoretical
Distributed artificial intelligence
Week 13 - Theoretical
Contemporary applications of AI.
Week 14 - Theoretical
Contemporary applications of AI.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%15
Final Examination1%60
Quiz4%15
Assignment5%5
Term Assignment1%5
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140228
Assignment50210
Project18715
Quiz45022
Midterm Examination116925
Final Examination120222
TOTAL WORKLOAD (hours)150
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
4
5
4
4
3
3
3
4
2
3
2
OÇ-2
5
5
4
4
4
2
3
4
2
3
3
OÇ-3
5
4
4
5
5
2
3
4
3
3
3
OÇ-4
5
5
5
5
5
3
4
5
3
4
3
OÇ-5
5
4
4
4
3
2
3
4
3
3
3
OÇ-6
5
5
5
5
5
3
4
5
3
4
4
Adnan Menderes University - Information Package / Course Catalogue
2026