CEN348 Artificial Intelligence Systems

5 ECTS - 3-0 Duration (T+A)- 6. Semester- 3 National Credit

Information

Code CEN348
Name Artificial Intelligence Systems
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 Goal

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