CE 344 | Course Introduction and Application Information

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;
  • will be able to describe a broad range of advanced machine learning techniques,
  • will be able to apply advanced machine learning techniques and algorithms,
  • will be able to compare different techniques and algorithms in advanced machine learning,
  • will be able to design machine learning algorithms for specific practical problems,
  • will be able to evaluate practical applications of advanced machine learning techniques.
Course Content 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.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
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.

 

EVALUATION SYSTEM

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 End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

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

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
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 multi-disciplinary 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