TÜRKÇE
ENGLISH
Information
Course Goal / Objective
The aim of this course is to teach students the basic concepts and techniques of artificial intelligence and to provide them with the ability to understand and use artificial intelligence applications at a basic level.
Course Content
his course provides a basic introduction to artificial intelligence, exploring its history, applications, and importance in various fields. It also covers fundamental concepts such as machine learning, deep learning, supervised and unsupervised learning, as well as the working principles and applications of artificial intelligence algorithms.
Course Precondition
None
Resources
Lecture notes to be given by the instructor
Notes
Lecture notes to be given by the instructor
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understands the concepts of artificial intelligence and can grasp its usage areas. |
| LO02 | Understands data processing methods used in the field of artificial intelligence and can prepare data sets |
| LO03 | Understands machine learning and deep learning techniques and can apply basic algorithms. |
| LO04 | Understands machine learning and deep learning methods and has the ability to apply the basic algorithms of these methods |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Defines the concept of health informatics. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains the types and sources of health data. | |
| PLO03 | Belirsiz | Analyzes the processing, storage and sharing of health data | |
| PLO04 | Belirsiz | Summarizes the structure and function of health information systems. | |
| PLO05 | Belirsiz | Evaluates the effects of digitalization in healthcare. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Definition and Historical Development of Artificial Intelligence | lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | The Use and Importance of Artificial Intelligence in Different Fields | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Introduction to Machine Learning | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Supervised and Unsupervised Learning | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Data Preparation | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Classification Algorithms | lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Clustering Algorithms | lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Lecture Notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Expert Systems | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Expert Systems II | lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Fuzzy Logic 1 | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Fuzzy | Lecture Notes | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 13 | Genetic Algorithms | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Artificial Neural Networks 1 | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | Artificial Neural | Lecture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 16 | Term Exams | Lecture Notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Lecture Notes | Ölçme Yöntemleri: Yazılı Sınav |
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 | 0 | 0 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 10 | 10 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 66 | ||
| Total Workload / 25 (h) | 2,64 | ||
| ECTS | 3 ECTS | ||