Course Name Code Semester T+U Hours Credit ECTS
Artifical Intelligence BSM 310 6 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Compulsory
Course Coordinator Dr.Öğr.Üyesi İSMAİL ÖZTEL
Course Lecturers Dr.Öğr.Üyesi İSMAİL ÖZTEL,
Course Assistants
Course Category
Course Objective The aim of this course is to give basic introduction to Artificial intelligent. In this course we will study artificial intelligent algorithms. Students will also learn how to solve their problems using artificial intelligent algorithms.
Course Content Learn the basic AI techniques, the problems for which they are applicable and their limitations. Topics covered include search (solving puzzles, playing games), planning, logical inference (drawing conclusions from data), expert systems, natural language processing and machine learning
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Understand basic concepts and techniques of Artificial intelligence Lecture, Self Study, Testing,
2 Understand AI by doing it, i.e. developing machine and programs acting like human and animals. Self Study, Drilland Practice, Lecture, Testing,
3 Comprehend common methods of AI and how to use them. Self Study, Case Study, Drilland Practice, Lecture, Testing, Homework,
4 Comprehend how to solve engineering problems with artificial intelligent algorithms Project Based Learning, Simulation, Group Study, Lecture, Homework, Oral Exam, Project / Design,
Week Course Topics Preliminary Preparation
1 Introduction to Artificial Intelligent
2 Search algorithms
3 Search algorithms
4 Heuristic search algorithms
5 Heuristic search algorithms
6 Heuristic search algorithms and using them in game programming
7 Expert system
8 Expert system
9 Artificial neural networks
10 Artificial neural networks
11 Intelligent agent
12 Genetic algorithm
13 Artificial intelligent applications
14 Student presentations
Course Notes Artifical Intelligence Lecture Notes
Course Resources 1.Stuart Russell, Peter Norvig; Artificial Intelligence A Modern Approach, Prentice-Hall, Inc., 1995.
2.Ivan Bratko, Prolog programming for Artificial Intelligence, Addison Wesley Publisher limited, 2001.
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions, X
2 An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations X
3 An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently, X
4 An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods, X
5 An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research,
6 Understanding, awareness of administration, control, development and security/reliability issues about information technologies, X
7 An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility, X
8 An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language, X
9 An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology, X
10 Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society, X
11 An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications, X
12 An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 40
1. Kısa Sınav 10
1. Ödev 25
2. Ödev 25
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 8 8
Quiz 2 3 6
Assignment 2 8 16
Final examination 1 8 8
Total Workload 134
Total Workload / 25 (Hours) 5.36
dersAKTSKredisi 5