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
Oral Exams
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

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical 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
Oral Exam
Midterms
1
15
Final Exam
1
22
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

X
2

To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

X
4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software 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 Software Engineering problems.

X
6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

X
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 be able to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

X
9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

X
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.

X
11

To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language.

X
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 Software Engineering.

X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest