Course Details

ARTIFICIAL INTELIGENCE

COMP465

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
7COMP465ARTIFICIAL INTELIGENCE0+3+035

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Gain an understanding of artificial intelligence methodologies
Learn the techniques used for developing artificial intelligence models
Gain practice by coding programming assignments
Apply the concepts to a real problem by completing a course project
Course Content This course provides an introduction to Artificial Intelligence. In this course, we will learn the concepts that underlie intelligent systems. Topics we will cover include problem solving with search, constraint satisfaction, knowledge representation and reasoning using some probabilistic learnings and first order logics, reasoning under uncertainty, introduction to machine learning, and introduction to reinforcement learning.
Course Methods and Techniques In this course, search algorithms, constraint satisfaction problems, probabilistic inference methods and logical representation techniques used to solve artificial intelligence problems will be covered. In addition, basic machine learning algorithms and introductory reinforcement learning methods will be examined. Students will experience these methods in practice through programming assignments and project work.
Prerequisites and co-requisities None
Course Coordinator Associate Prof.Dr. Zafer Aydın - zafer.aydin@agu.edu.tr
Name of Lecturers Associate Prof.Dr. Zafer Aydın - zafer.aydin@agu.edu.tr
Assistants Research Assist. Bölüm araştırma görevlileri. Bölüm araştırma görevlileri. Bölüm araştırma görevlileri.
Work Placement(s) No

Recommended or Required Reading
Resources Artificial Intelligence: A Modern Approach – Stuart Russell & Peter Norvig Artificial Intelligence: Foundations of Computational Agents – David L. Poole & Alan K. Mackworth Artificial Intelligence For Dummies (2nd Edition) – Luca Massaron & John Mueller
Artificial Intelligence: A Modern Approach – Stuart Russell & Peter Norvig
Artificial Intelligence: Foundations of Computational Agents – David L. Poole & Alan K. Mackworth
Artificial Intelligence For Dummies (2nd Edition) – Luca Massaron & John Mueller
Will be shared on Canvas.
Will be shared on Canvas.
Will be shared on Canvas.

Course Category
Mathematics and Basic Sciences %20
Engineering %30
Engineering Design %30
Social Sciences %10
Education %0
Science %10
Health %0
Field %0

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Ödev 5 % 50
Sunum/Seminer 1 % 50
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 1 10 10
F2F Dersi 1 3 3
Ev Ödevi 1 5 5
Sunum için Hazırlık 1 5 5
Proje 1 63 63
Kısa Sınav 1 1 1
Okuma 1 1 1
Senkron Ders 14 3 42
Final Sınavı 1 20 20
Total Work Load   Number of ECTS Credits 5 150

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the mathematical and algorithmic principles of artificial intelligence models
2 Solve a machine learning problem using artificial intelligence methods
3 Implement a reinforcement learning model using a software
4 Apply a deep learning method to a real problem


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Artificial Intelligence and Its History - -
2 Intelligent Agents and Problem Formulation - -
3 Search Methods in State Space - -
4 Informed Search and Heuristics - -
5 Decision Making in Games and the Minimax Algorithm - -
6 Constraint-Based Problem Solving - -
7 Logical Knowledge Representation and Inference - -
8 First-Order Logic and Unification - -
9 Reasoning Under Uncertainty: Bayesian Networks - -
10 Decision Trees and Probabilistic Reasoning - -
11 Introduction to Machine Learning: Supervised and Unsupervised Learning - -
12 Introduction to Reinforcement Learning - -
13 Applications of Artificial Intelligence and Ethical Discussions - -
14 Presentation of Course Projects and General Evaluation - -


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15
All 4 3 5 3 2 2 3 1 2 1 5 5 5 4 3
C1 5 2 4 2 1 2 3 1 1 1 5 5 5 4 2
C2 4 3 5 3 2 2 3 1 2 1 5 5 5 3 3
C3 3 2 4 3 2 2 3 1 1 1 5 4 5 3 3
C4 4 3 5 4 2 2 3 1 2 1 5 4 5 4 3

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=74870&lang=en
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