Course Name | Advanced Machine Learning |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
---|---|---|---|---|---|
CE 344 | Fall/Spring | 3 | 0 | 3 | 5 |
Prerequisites | None | |||||
Course Language | English | |||||
Course Type | Elective | |||||
Course Level | First Cycle | |||||
Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | ||||||
Course Coordinator | ||||||
Course Lecturer(s) | - | |||||
Assistant(s) | - |
Course Objectives | The objective of this course is to provide advanced knowledge on the state of the art in machine learning. Both fundamental and advanced properties of machine learning algorithms as well as practical applications will be discussed. |
Learning Outcomes | The students who succeeded in this course;
|
Course Description | The following topics will be included: training data collection, learning in order to extract statistical structure from data, over-fitting, parametric models and parameter selection, validation, regression, classification, nonparametric models, clustering. |
Related Sustainable Development Goals | |
| Core Courses | |
Major Area Courses | ||
Supportive Courses | X | |
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
1 | Introduction to machine learning. Probability review | Chapter 1-2. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
2 | Generative models for discrete data. Gaussian models | Chapter 3-4. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
3 | Bayesian and frequentist statistics | Chapter 5-6. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
4 | Linear and logistic regression | Chapter 7-8. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
5 | Generalized linear models and the exponential family | Chapter 9. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
6 | Graphical models: Markov random fields and Bayes nets | Chapter 10 and 19. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
7 | Mixture models and the EM algorithm | Chapter 11. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
8 | Latent linear and sparse linear models | Chapter 12-13. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
9 | Markov and hidden Markov models | Chapter 17. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
10 | Midterm exam | |
11 | Exact inference for graphical models. Variational inference. | Chapter 20-21-22. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
12 | Monte Carlo and Markov Chain Monte Carlo inference | Chapter 23-24. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
13 | Kernel models | Chapter 14. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
14 | Clustering | Chapter 25. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 |
15 | General course review | |
16 | General course review |
Course Notes/Textbooks | Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012, ISBN: 9780262018029 |
Suggested Readings/Materials | Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN: 9780387310732. |
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | 4 | 30 |
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | ||
Project | ||
Seminar / Workshop | ||
Oral Exam | ||
Midterm | 1 | 30 |
Final Exam | 1 | 40 |
Total |
Weighting of Semester Activities on the Final Grade | 5 | 60 |
Weighting of End-of-Semester Activities on the Final Grade | 1 | 40 |
Total |
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
Study Hours Out of Class | 15 | 3 | 45 |
Field Work | |||
Quizzes / Studio Critiques | 4 | 5 | |
Portfolio | |||
Homework / Assignments | |||
Presentation / Jury | |||
Project | |||
Seminar / Workshop | |||
Oral Exam | |||
Midterms | 1 | 15 | |
Final Exams | 1 | 22 | |
Total | 150 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To have adequate knowledge in Mathematics, Science and Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. | |||||
2 | To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. | X | ||||
3 | To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose. | |||||
4 | To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively. | X | ||||
5 | To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics. | X | ||||
6 | To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; to be able to work individually. | |||||
7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. | |||||
8 | To have knowledge about global and social impact of Computer Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Computer Engineering solutions. | |||||
9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. | |||||
10 | To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
11 | To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1) | |||||
12 | To be able to speak a second foreign language at a medium level of fluency efficiently. | |||||
13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Computer Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest