Faculty of Engineering and Natural Sciences · Software Engineering (English 30%) · Undergraduate
ECTS: 5 T+P+L: 2+0+1 Departmental Elective
Coordinator: Doç. Dr. Cevat REŞİT
Course Objective
The methods in machine learning and mathematical modelling of these methods on data are aimed.
Course Content
It includes concepts related to learning types in machine learning with applications. Supervised learning, Bayesian decision theory, dimension reduction, clustering, distribution-free methods, decision trees, linear classification, multilayer perceptrons, support vector machines algorithms will be covered with theoretical and practical applications.
Course Learning Outcomes
- It defines the fundamental concepts of machine learning, types of learning, and problem classes.
- Analyzes and explains the mathematical foundations of machine learning methods.
- Formulates real-world problems as machine learning problems and applies appropriate methods.
- Applies feature extraction, feature selection, and dimensionality reduction approaches appropriately to the problem.
- Analyzes and interprets the success and performance of the developed models using appropriate evaluation criteria.
- Explains machine learning solutions under privacy, security, and legal constraints (e.g., federated learning).
Core Area Distribution
(46) Mathematics and Statistics%50 (52) Engineering and Engineering Trades%50