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	-
National Occupation Classification	-
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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	Learn to use and discuss the basic techniques/algorithms of the field
2	Have knowledge of the advantages and limitations of different machine learning algorithms
3	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	Learning Outcome
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	Weighting	LO 1	LO 2	LO 3
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

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	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.		-	-	-	-	-