Machine Learning Fundamentals
Software Engineering
Faculty of Engineering
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Machine Learning Fundamentals | YZM324 | Elective | Bachelor's degree | 3 | Spring | 5 |
Name of Lecturer(s)
Assistant Prof. Dr. Kaplan KAPLAN
Learning Outcomes of the Course Unit
1) Knows the fundamentals of machine learning algorithms and comprehends the computational logic.
2) Knows the terms used to measure learning performance.
3) Understand the classification, clustering and association analysis.
4) Understands the types of learning.
5) Knows the concept of machine learning.
6) Explain the concepts of data and information and know their differences.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
| Learning Outcomes | ||||||||||
| 1 | High | High | No relation | Middle | Middle | Middle | Middle | Middle | Middle | |
| 2 | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | |
| 3 | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | |
| 4 | High | High | High | High | No relation | No relation | No relation | No relation | No relation | |
| 5 | High | High | No relation | High | No relation | No relation | No relation | No relation | No relation | |
| 6 | No relation | No relation | No relation | No relation | High | No relation | High | High | High | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Introduction to machine learning, Supervised (supervised, teacher) learning, classification algorithms, Support vector machines, decision trees, Layout and model selection, Online learning and perceptron algorithm, Unsupervised learning, k-mean clustering, Gaussian blend model, Maximum expectation algorithm, Principal component analysis, Independent component analysis, Reinforcement learning.
Weekly Schedule
1) Fundamentals of Machine Learning2) Linear Regression, Least Squares Algorithm
3) Clustering-Classification
4) Logistic Regression
5) Bayesian Learning
6) Support Vector Machines
7) Decision Trees
8) Neural Networks
9) Neural Networks
10) Ensemble Learning Methods: Random Forest, Boosting algorithms
11) Principal Component Analysis
12) Convolutional Neural Networks
13) Project Presentations
14) Project Presentations
15) Final Exam
Recommended or Required Reading
- Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".
- 2. Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, first edition 2004, second edition 2010.
- Sorhun, E. (2021). Machine Learning with Python. Istanbul: Abakus Yayincilik.
- Igor Kononenko, Matjaz Kukar (2007) Machine Learning and Data Mining: Introduction to Principles and Algorithms, Horwood Publishing Limited, 454 pages.
- Goodfellow, I. (2016). Deep learning (Vol. 196). MIT press.
Planned Learning Activities and Teaching Methods
1) Lecture
2) Lecture
3) Lecture
4) Question-Answer
5) Question-Answer
6) Question-Answer
Assessment Methods and Criteria
Contribution of Project to Course Grade |
30% |
|---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required