Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BBM405FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE3+0+03606.09.2024

 
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 The aim is to provide insight to the main principles underlying the human intelligence and machine intelligence. The students will acquire hands-on experience about main AI problems and their fundamental solution techniques.
Course Content Problem-solving techniques: state-space approach, problem-reduction approach, exhaustive search algorithms, heuristic search algorithms, game playing algorithms and game trees, knowledge representation and reasoning, learning in AI systems, artificial neural networks, proof theory of propositional logic, first-order predicate logic, Bayesian networks, semantic nets, fuzzy logic, perception, robotics.
Course Methods and Techniques Lecture, Drill and Practice, Problem Solving
Prerequisites and co-requisities ( BBM104 ) and ( BBM102 )
Course Coordinator None
Name of Lecturers Prof. Dr. 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), Prentice-Hall, 2009.
Course Notes Russell S. ve Norvig P., Artificial Intelligence: A Modern Approach (AIMA), Prentice-Hall, 2009.


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 6 % 30
GenelSınav 1 % 40
Total
8
% 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 6 12 72
Preparation for Midterm Exam 1 15 15
General Exam Preparation 1 25 25
Total Work Load   Number of ECTS Credits 5,8 174

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 In completition of this course, the student will have the fundamental knowledge on principles of artificial intelligence.
2 The student will develops problem solving skills on various artificial intelligence problems and implements related software.
3 The student will through brief research to the current literature over the subject, acquires knowledge about the latest emerging technology.
4 The student will have a general knowledge about the basic robotics systems.
5 Basit robot sistemlerinin genel mantığı konusunda bilgi sahibi olur.
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to the Project
2 Intelligent agents
3 Problem solving by search and informed search algorithms
4 Heuristic search algorithms
5 Game algorithms
6 Game trees
7 Logic programming
8 Midterm Exam
9 First-order Logic
10 Knowledge representation
11 Probabilistic reasoning
12 Learning from observations
13 Fundamentals of statistical learning methods
14 Fundamentals of perception, robotics and 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 3 3 3 4 3 5 3 1 4
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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