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Information
| Unit | ADANA VOCATIONAL SCHOOL |
| Code | BPP239 |
| Name | Artificial intelligence |
| Term | 2022-2023 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 | Ön Lisans Dersi |
| 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 | Able to define artificial intelligence |
| LO02 | Understanding the structure of artificial intelligence algorithms and systems |
| LO03 | Able to define brain function and neuron structures |
| LO04 | Able to establish network structures according to types and functions of neural networks |
| LO05 | Understanding of deep learning and expert systems |
| LO06 | Understanding concepts such as fuzzy logic systems, genetic algorithms, and ant colony algorithms |
| LO07 | Ability to make basic level artificial intelligence applications |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Artificial Intelligence | Review of Source Book | |
| 2 | Artificial Neural Networks and Basic Elements | Learning about artificial neural networks | |
| 3 | Creation of Artificial Neural Networks | Learning about artificial neural networks | |
| 4 | Structures of Artificial Neural Networks | Research about structures of artificial neural networks | |
| 5 | Supervised Learning | Research about learning methods | |
| 6 | Unsupervised Learning | Research about learning methods | |
| 7 | Deep Networks and Deep Learning | Research about deep learning | |
| 8 | Mid-Term Exam | Preparing for the exam | |
| 9 | Introduction to Fuzzy Logic | Research about fuzzy logic | |
| 10 | Crisp Sets and Fuzzy Sets | Learning about sets | |
| 11 | Genetic Algorithms | Learning about genetic | |
| 12 | Ant Colony Algorithms | Research about ant colony behavior | |
| 13 | Expert Systems | Research about expert systems | |
| 14 | Machine Learning | Research about machine learning | |
| 15 | Applications of Artificial Intelligence | Researching programming languages used in artificial intelligence applications | |
| 16 | Term Exams | Preparing the exam | |
| 17 | Term Exams | Preparing the exam |
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 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 78 | ||
| Total Workload / 25 (h) | 3,12 | ||
| ECTS | 3 ECTS | ||