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Information
| Unit | ADANA VOCATIONAL SCHOOL |
| Code | BPP231 |
| Name | Artificial Intelligence Applications |
| Term | 2018-2019 Academic Year |
| Semester | 3. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Öğr. Gör. Dr. YILMAZ KOÇAK |
| Course Instructor |
Öğr. Gör. Dr. YILMAZ KOÇAK
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To learn definition and methods of artificial intelligence, to learn general structure of intelligent algorithms, to have knowledge about artificial neural networks, deep learning, expert systems, fuzzy logic techniques. To prepare basic artificial intelligence applications with artificial intelligence programs (Python, Matlab, etc.).
Course Content
Definition of artificial intelligence, algorithms and general structure of systems, expert systems, artificial neural networks, deep networks and deep learning, crips logic and fuzzy logic, software tools using artificial intelligence.
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Define artificial intelligence |
| LO02 | Understand the structure of artificial intelligence algorithms and systems |
| LO03 | Define brain function and neuron structures |
| LO04 | Establish network structures according to types and functions of neural networks |
| LO05 | understand of deep learning and expert systems |
| LO06 | Understand fuzzy logic systems and algorithms |
| LO07 | Prepare beginners level applications |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Artificial Intelligence | Getting information from source books | |
| 2 | Artificial Neural Networks and Basic Elements | Review of previous course notes and getting information from source books | |
| 3 | Creation of Artificial Neural Networks | Read the subject of artificial neural networks | |
| 4 | Structures of Artificial Neural Networks | Read the subject of artificial neural networks | |
| 5 | Supervised Learning | Read the subject of supervised learning | |
| 6 | Unsupervised Learning | Read the subject of supervised learning | |
| 7 | Deep Networks and Deep Learning | Review of subjects of deep learning and deep networks | |
| 8 | Mid-Term Exam | Exam preparation | |
| 9 | Introduction to Fuzzy Logic | Review of the subject of fuzzy logic | |
| 10 | Crisp Sets and Fuzzy Sets | Review of Crisp and fuzzy sets subjects | |
| 11 | Applications of Fuzzy Logic | Review of the application of Fuzzy Logic | |
| 12 | Genetics Algorithms | Review of genetic algorithms | |
| 13 | Expert Systems | Get information about expert systems | |
| 14 | Machine Learning | Get information about machine learning | |
| 15 | Applications of Artificial Intelligence | Review of the applications of Artificial Intelligence | |
| 16 | Term Exams | Exam preparation | |
| 17 | Term Exams | Exam preparation |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |