CMP5103 Artificial IntelligenceBahçeşehir UniversityDegree Programs ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP5103 Artificial Intelligence Fall 3 0 3 8
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi TEVFİK AYTEKİN
Course Lecturer(s): Prof. Dr. NAFİZ ARICA
Recommended Optional Program Components: None
Course Objectives: The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems.

Learning Outcomes

The students who have succeeded in this course;
I. Be able to solve problems by applying a suitable search method.
II. Be able to implement minimax search and alpha-beta pruning in game playing.
III. Be able to use logical formalisms in modeling.
IV. Be able to apply supervised learning techniques to a given problem.
V. Be able to apply unsupervised learning techniques to a given problem.
VI. Be able to use the basic techniques in natural language processing.

Course Content

introduction; uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction
2) Uninformed Search Strategies
3) Uninformed Search Strategies
4) Informed (Heuristic) Search Strategies
5) Informed (Heuristic) Search Strategies
6) Adversarial Search
7) Propositional Logic
8) Predicate logic
9) Supervised Learning Techniques
10) Supervised Learning Teknileri
11) Unsupervised Learning Techniques
12) Unsupervised Learning Techniques
13) Natural Language Processing
14) Natural Language Processing
15) Final Exam


Course Notes / Textbooks: Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009.
Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Presentation 1 % 10
Project 1 % 50
Final 1 % 40
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 3 42
Project 1 42 42
Homework Assignments 7 8 56
Final 1 12 12
Total Workload 194

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution