Faculty of Engineering and Natural Sciences · Computer Engineering · Undergraduate
ECTS: 5 T+P+L: 2+0+1 Departmental Elective
Coordinator: Dr. Öğr. Üyesi Şengül BAYRAK
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
Teaching Methods
ExpressionQuestion-AnswerDiscussionExercise and PracticeGroup StudyBrain StormingSelf studyProblem SolvingProject Based Learning (Including Field Work)
Assessment & Evaluation
HomeworkPerformance Assignment ( Lab / Workshop / Field Work / Seminar / Presentation / Completion Study / ThesisProject / DesignTesting (Essay / Tests: True-Falls, multiple-choice, short answer, matching)
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 4 | 64 |
| Out of Class Study Period | 1 | 10 | 10 |
| Midterm | 1 | 2 | 2 |
| Quiz | 0 | 0 | 0 |
| Assignment | 2 | 10 | 20 |
| Practice | 1 | 15 | 15 |
| Final | 1 | 2 | 2 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | What is Machine Learning? Machine learning types and application areas | - |
| 2 | Data types, data preprocessing techniques, data exploration, machine learning system design | Week 1 |
| 3 | Dimension Reduction and its application | Week 2 |
| 4 | BayesianDecision Theory and Applications | Week 3 |
| 5 | Clustering and its applications | Week 4 |
| 6 | Distribution-independent methods (k nearest neighbor algorithm) | Week 5 |
| 7 | Linear regression method and application areas | Week 6 |
| 8 | Midterm Exam | Week 1-7 |
| 9 | Decision Trees and its applications | Week 8 |
| 10 | Machine learning solutions under privacy, security, and legal constraints (e.g., federated learning) | Week 9 |
| 11 | Multi Layer Perceptron and its applications | Week 10 |
| 12 | Multilayer perceptions (Training a Perceptron, Multilayer Perceptron, Backpropagation Algorithm) | Week 11 |
| 13 | Kernel Machines and its applications | Hafta 12 |
| 14 | Project presentations | Week 13 |
| 15 | Project presentations | Week 14 |
| 16 | Final Exam | Week 1-15 |