GRADUATE SCHOOL

M.SC. in Electrical and Electronics Engineering (With Thesis)

IES 511 | Course Introduction and Application Information

Course Name
Machine Learning
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IES 511
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Second Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator -
Course Lecturer(s) -
Assistant(s) -
Course Objectives The field of machine learning is concerned with the question of how to construct computer programs that improve automatically with experience. In recent years, many successful applications of machine learning have been developed, ranging from datamining programs that learn to detect fraudulent credit card transactions, to autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundation of this field. The goal of this course is to provide an overview of the stateofart algorithms used in machine learning. Both the theoretical properties of these algorithms and their practical applications will be discussed.
Learning Outcomes The students who succeeded in this course;
  • Learn to use and discuss the basic techniques/algorithms of the field
  • Have knowledge of the advantages and limitations of different machine learning algorithms
  • Be able to evaluate potential applications of Machine Learning techniques
Course Description Supervised Learning: Decision trees, nearest neighbors, linear classifiers and kernels, neural networks, linear regression; learning theory. Unsupervised Learning: Clustering, graphical models, EM, PCA, factor analysis. Reinforcement Learning: Value iteration, policy iteration, TD learning, Q learning. Bayesian learning, online learning.

 



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, Machine Learning examples T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 1)
2 Concept Learning, Inductive Bias T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 2)
3 Decision Trees T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 3)
4 Artificial Neural Networks T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 4)
5 Bayesian Learning T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 6)
6 Instance Based Learning T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 8)
7 Ara sınav / Midterm
8 Reinforcement Learning T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 13)
9 Evolutionary Algorithms T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 9)
10 Learning Theory T. Mitchell, Machine Learning;, McGrawHill, 1997, hardcover ISBN 0070428077 (Ch. 7)
11 Discussions, Research and Presentations
12 Discussions, Research and Presentations
13 Discussions, Research and Presentations
14 Discussions, Research and Presentations
15 Review for Final Exam
16 Review of the Semester  

 

Course Notes/Textbooks The textbook referenced above and course slides
Suggested Readings/Materials 1) Introduction to Machine Learning, Ethem Alpaydın, The MIT Press, October 2004, ISBN 0262012111. 2) Related Research Papers

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
1
20
Project
1
25
Seminar / Workshop
Oral Exams
Midterm
1
30
Final Exam
Total

Weighting of Semester Activities on the Final Grade
75
Weighting of End-of-Semester Activities on the Final Grade
25
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
0
Study Hours Out of Class
15
6
90
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
1
20
20
Project
1
25
25
Seminar / Workshop
0
Oral Exam
0
Midterms
1
15
15
Final Exam
0
    Total
198

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering; evaluates, interprets and applies information
2 Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations
3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines
4 Is informed about new and upcoming applications in the field and learns them whenever necessary.

5 Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions.
6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.
7 Designs and implements studies based on theory, experiments and modeling; analyses and resolves the complex problems that arise in this process.
8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility.
9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
11 Is knowledgeable about the social, environmental, health, security and law implications of Electrical and Electronics Engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics Engineering applications.
12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. Adheres to the principles of research and publication ethics.

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

 


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