Machine Learning(VM 532)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| VM 532 | Machine Learning | 2 | 4 | 0 | 0 | 3 | 8 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Compulsory |
| Course Level | Masters Degree |
| Course Instructor(s) | Ayberk ZEYTİN azeytin@gsu.edu.tr (Email) |
| Assistant | |
| Objective | The objective of this course is to provide students with a solid foundation in machine learning and deep learning. By covering both theoretical concepts and practical applications, students will learn to design, implement, and evaluate various machine learning models for solving real-world problems. |
| Content | Course content includes an introduction to machine learning, mathematical foundations, the deep relationship between optimization and machine learning, problems encountered in optimization and their solutions, training processes of different models, frequently encountered problems and their solutions, and practical project work. |
| Course Learning Outcomes | Upon completion of the course, students will be able to understand the principles of machine learning and deep learning, apply various ML techniques to solve problems, implement models using Python and relevant libraries, and develop their own projects demonstrating their learning. |
| Teaching and Learning Methods | Python notebooks, slides, projects. |
| References |
https://udlbook.github.io/udlbook/ https://www.amazon.com/Hundred-Page-Machine-Learning-Book/dp/199957950X https://www.di.ens.fr/appstat/spring-2023/ |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Overview of machine learning, types of learning, and applications. |
| 2 | Minimal reusable ML data pipeline and data leakage |
| 3 | Optimization foundations |
| 4 | Training = minimizing a loss |
| 5 | Gradient descent for univariate functions |
| 6 | Gradient descent for multivariate functions |
| 7 | Saddle point problem and higher order methods |
| 8 | Regularization |
| 9 | Model Toolbox I : Regression, forecasting and binary classification |
| 10 | Model Toolbox II : Multiclass classification, decision trees and random forests |
| 11 | Model Toolbox III : Gradient boosting with trees, XGBoost, LightGBM and Clustering / Segmentation |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 50 |
| Contribution of final exam to overall grade | 1 | 50 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 50 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Toplam | 1 | 50 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 42 | 588 |
| Working Hours out of Class | 0 | 0 | 0 |
| Assignments | 0 | 0 | 0 |
| Presentation | 0 | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 3 | 3 |
| Project | 1 | 10 | 10 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Workload | 601 | ||
| Total Workload / 25 | 24.04 | ||
| Credits ECTS | 24 | ||