Introduction To Machine Learning
Information Systems Engineering
Faculty of Technology
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 |
|---|---|---|---|---|---|---|
| Introduction To Machine Learning | TBL322 | Elective | Bachelor's degree | 3 | Spring | 5 |
Name of Lecturer(s)
Prof. Dr. Halil YİĞİT
Associate Prof. Dr. Zeynep Hilal KİLİMCİ
Learning Outcomes of the Course Unit
1) Understands the basic concepts of machine learning
2) Learn and apply the necessary models for machine learning.
3) Understands and applies other learning models intertwined with machine learning.
4) Have an idea about the applications of machine learning methods in different fields.
5) Master the relevant programming language to implement machine learning models.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | High | High | High | Low | Low | High | Middle | Low | Middle | Middle | No relation | |
| 2 | High | High | High | Low | Low | High | Middle | Low | Middle | Middle | No relation | |
| 3 | High | High | High | Low | Low | High | High | Low | Low | Middle | No relation | |
| 4 | High | High | High | High | High | High | High | High | High | High | High | |
| 5 | High | High | High | Low | Low | High | High | Middle | Middle | High | Low | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Statistics and Probability, Data Mining
Course Contents
-Understanding machine learning algorithms, - Introduction of other learning methods under machine learning, - Realization of machine learning models on different subjects, - Presenting new generation learning models, - Application of the models mentioned in the course content within the scope of the project.
Weekly Schedule
1) Basic Concepts2) Supervised Machine Learning and Methods
3) Supervised Machine Learning and Methods
4) Supervised Machine Learning and Methods
5) Supervised Machine Learning and Methods
6) Deep Learning and Methods
7) Deep Learning and Methods
8) Midterm Examination
9) Deep Learning and Methods
10) Reinforcement Learning and Methods
11) Reinforcement Learning and Methods
12) Community Learning
13) Community Learning
14) Project Presentations
15) Project Presentations
16) Final Examination
Recommended or Required Reading
1- Andriy Burkov, Machine Learning Engineering
2- Andreas C. Müller, Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists
3- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Group Study
6) Self Study
7) Project Based Learning
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
40% |
|---|---|
Contribution of Final Examination to Course Grade |
60% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required