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Information
| Code | CEN348 |
| Name | Artificial Intelligence Systems |
| Term | 2023-2024 Academic Year |
| Semester | 6. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. MUSTAFA ORAL |
| Course Instructor |
Mehmet SARIGÜL
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Knowledge representation. Search and intuitive programming. Logic and logic programming. Applications of artificial intelligence: Problem solving, games and puzzles, expert systems, planning, learning, pattern recognition, natural language understanding.
Course Content
Representation of knowledge. Search and heuristic programming. Logic and logic programming. Application areas of artificial intelligence: Problem solving, games and puzzles, expert systems, planning, learning, vision, and natural language understanding. Exercises in an artificial intelligence language
Course Precondition
None
Resources
1 Nabiyev V. V., 2005 Yapay Zeka: Problemler, Yöntemler, Algoritmalar, Ankara (2. Baskı) 2 Russell, Stuart J. ; Norvig, Peter, 2003 , Artificial Intelligence: A Modern Approach (2nd ed. )
Notes
1 Nilsson, Nils,1998 , Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Make comparisons between artificial and natural intelligence |
| LO02 | Have knowledge about the main problems of artificial intelligence |
| LO03 | Can decide which of the basic search or heuristic search techniques to give priority in solving various problems. |
| LO04 | Makes information modeling and programs on the computer |
| LO05 | By understanding how basic behavior patterns such as speech, natural language and learning are modeled in computer applications, they can apply basic approaches, ANN, with genetic algorithms. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Can access information,gains the ability to do resource research and uses information resources | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | 2 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | 3 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Professional and ethical responsibility, | 1 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Artificial Intelligence | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Intelligent Agents | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Problem Solving and Search | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Heuristic Problem Examples | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Pattern Recognition | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Expert Systems | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 7 | Learning and Neural Networks | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 8 | Mid-Term Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Fuzzy Logic | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 10 | Evolutionary Methods | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Genetic Algorithm | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 12 | Ant Colony Algorithm | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Project Presentations 1 | Reading the lecture notes | Öğretim Yöntemleri: Gösterip Yaptırma, Bireysel Çalışma, Proje Temelli Öğrenme |
| 14 | Project Presentations 2 | Reading the lecture notes | Öğretim Yöntemleri: Gösterip Yaptırma, Proje Temelli Öğrenme , Bireysel Çalışma |
| 15 | Problem Solving | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap, Örnek Olay |
| 16 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 114 | ||
| Total Workload / 25 (h) | 4,56 | ||
| ECTS | 5 ECTS | ||