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) | Gönenç ONAY gonay@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 | The course content includes an introduction to machine learning, mathematical foundations, deep learning basics, training models, convolutional and recurrent neural networks, advanced models like GANs and autoencoders, natural language processing, 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 | Lineer and logistic regression. |
| 3 | Introduction to Python programming for ML, libraries (NumPy, Pandas). |
| 4 | Neural Networks Basics - Understanding neural networks, activation functions, and architecture. |
| 5 | Deep Learning Fundamentals - Introduction to deep learning, frameworks, and setting up the environment. |
| 6 | Training Deep Neural Networks - Techniques for training DNNs, avoiding overfitting, and regularization. |
| 7 | Convolutional Neural Networks (CNNs) - Basics of CNNs, applications in image recognition. |
| 8 | Midterm Exam - Assessment covering all previously seen topics |
| 9 | Recurrent Neural Networks (RNNs) - Introduction to RNNs, LSTM, and their applications. |
| 10 | Advanced Deep Learning Models - Exploring GANs, autoencoders, and reinforcement learning basics. |
| 11 | Deep Learning for Sequential Data - Time series analysis, RNNs for sequence data. |
| 12 | Natural Language Processing with Deep Learning - Techniques and models for NLP. |
| 13 | Project Discussions - Students present their projects, discussion, and feedback. |
| 14 | Project Presentations - Final presentation of projects, course wrap-up, and future directions. |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 2 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 3 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 1 | 30 |
| Midterm Examinations (including preparation) | 1 | 30 |
| 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 |
| Toplam | 2 | 60 |
| 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 | ||