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


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. 
Course Description 
The students who succeeded in this course;

Course Content  The following topics will be included: training data collection, learning in order to extract statistical structure from data, overfitting, parametric models and parameter selection, validation, regression, classification, nonparametric models, clustering. 

Core Courses  
Major Area Courses  
Supportive Courses  
Media and Management Skills Courses  
Transferable Skill Courses 
Week  Subjects  Related Preparation 
1  Introduction to machine learning. Probability review  Chapter 12. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 
2  Generative models for discrete data. Gaussian models  Chapter 34. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 
3  Bayesian and frequentist statistics  Chapter 56. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 
4  Linear and logistic regression  Chapter 78. 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 1213. 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 202122. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 
12  Monte Carlo and Markov Chain Monte Carlo inference  Chapter 2324. 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

Homework / Assignments  
Presentation / Jury  
Project  
Seminar / Workshop  
Portfolios  
Midterms / Oral Exams 
1

30

Final / Oral Exam 
1

40

Total 
Weighting of Semester Activities on the Final Grade  5 
60 
Weighting of EndofSemester 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


Field Work  
Quizzes / Studio Critiques 
4

5


Homework / Assignments  
Presentation / Jury  
Project  
Seminar / Workshop  
Portfolios  
Midterms / Oral Exams 
1

15


Final / Oral Exam 
1

22


Total 
150

#

Program Competencies/Outcomes 
* Contribution Level


1 
2 
3 
4 
5 

1  Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems  
2  Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose  
3  Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose  
4  Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice  
5  Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems  
6  Ability to work efficiently in Software Engineering disciplinary and multidisciplinary teams; ability to work individually  
7  Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages  
8  Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself  
9  Awareness of professional and ethical responsibility  
10  Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development  
11  Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions 
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest