Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
4AIN211Principles of Artificial Intelligence3+2+04612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course This course aims to present the main concepts and techniques used in Artificial Intelligence and introduce a range of real world AI applications. The students will acquire knowledge about the history and the foundations of AI, and the basis required for developing autonomous intelligent agents.
Course Content Problem-solving agents, uninformed search algorithms, heuristic search algorithms, game playing algorithms, constraint satisfaction problems, logical agents, propositional and first order logic, inference and decision making, knowledge representation and reasoning, planning, uncertainity and probabilistic reasoning, Bayesian networks, reinforcement learning, perception, robotics and computer vision.
Course Methods and Techniques Lecture
Practice
Problem solving
Prerequisites and co-requisities ( BBM101 ) and ( BBM103 )
Course Coordinator None
Name of Lecturers Prof. Pınar Duygulu Şahin
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Russell S. ve Norvig P., Artificial Intelligence: A Modern Approach (AIMA), 3rd edition, Prentice-Hall.
Course Notes Russell S. ve Norvig P., Artificial Intelligence: A Modern Approach (AIMA), 3rd edition, Prentice-Hall.


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
Midterm Exam 1 % 30
Assignment 1 % 30
Final examination 1 % 40
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 10 2 20
Assignments 5 15 75
Preparation for Midterm Exam 1 18 18
General Exam Preparation 1 25 25
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 By the end of the course students are expected to 1. Have the fundamental knowledge on principles of artificial intelligence. 2. Develop problem solving skills on various artificial intelligence problems and implement related applications. 3. Have a ge
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3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to concepts and history of AI, Intelligent agents
2 Problem solving by searching, informed search algorithms
3 Heuristic search algorithms
4 Constraint satisfaction problems
5 Adversarial search and games
6 Logical agents
7 Midterm exam
8 First Order Logic
9 Knowledge representation and logical reasoning
10 Planning
11 Uncertainty and Probabilistic Reasoning
12 Bayes Networks
13 Reinforcement learning
14 Fundamentals of perception, robotics and computer vision
15 Final exam preparation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 4 3 4 3 5 4 3 5 3 2 4 5
C1
C2
C3
C4
C5
C6
C7
C8

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

  
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