Faculty of Engineering and Natural Sciences · Industrial Engineering (English 30%) · Undergraduate
ECTS: 5 T+P+L: 3+0+0 Departmental Elective
Coordinator: Doç. Dr. Sinem GÜLER
Course Objective
To provide learners with the ability to understand machine learning, data mining algorithms and statistical learning methods as well as ideas and understanding, as well as how, why, and when to use them theoretically and practically.
Course Content
Various methods of Data Mining will be explained: - Naive Bayesian Networks - Decision Tree - Reinforcement Learning - Deep Learning - Neural Networks - Genetic Algorithms
Course Learning Outcomes
- Students will be able to make artificial intelligence-related projects by learning machine learning techniques.
Core Area Distribution
(46) Mathematics and Statistics%35 (48) Computing%65
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 0 | 0 | 0 |
| Out of Class Study Period | 0 | 0 | 0 |
| Midterm | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Assignment | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Final | 0 | 0 | 0 |