CE 531 | Course Introduction and Application Information - M.SC. in Computer Engineering (Without Thesis)

Course Name

Machine Learning

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 531	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	Dr. Öğr. Üyesi Kaya OĞUZ
Course Lecturer(s)	Dr. Öğr. Üyesi Kaya OĞUZ
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 data-mining 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 state-of-art 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;

will be able to distinguish between a range of machine learning techniques.
will be able to apply the basic techniques/algorithms of the field.
will be able to compare various techniques/algorithms of the field.
will be able to design and adapt various Machine Learning algorithms to specific situations.
will be able to evaluate potential applications of Machine Learning techniques.

Course Description

Machine learning is concerned with computer programs that automatically improve their performance with past experiences. Machine learning draws inspiration from many fields, artificial intelligence, statistics, information theory, biology and control theory. The course will cover the following topics;concept learning,decision tree learning ,artificial neural networks , instance based learning,evolutionary algorithms ,reinforcement learning ,Bayesian learning , computational learning theory

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

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction	Introduction to Machine Learning, Ethem Alpaydın, Chapters 1 and 2
2	Bayesian Decision Theory	Introduction to Machine Learning, Ethem Alpaydın, Chapter 3
3	Parametric Methods	Introduction to Machine Learning, Ethem Alpaydın, Chapter 4
4	Dimensionality Reduction	Introduction to Machine Learning, Ethem Alpaydın, Chapter 6
5	Clustering	Introduction to Machine Learning, Ethem Alpaydın, Chapter 7
6	Nonparametric Methods	Introduction to Machine Learning, Ethem Alpaydın, Chapter 8
7	Decision Trees	Introduction to Machine Learning, Ethem Alpaydın, Chapter 9
8	Midterm
9	Multilayer Perceptron	Introduction to Machine Learning, Ethem Alpaydın, Chapter 11
10	Stochastic Methods	Pattern Classification, Duda & Hart & Stork, Chapter 7
11	Kernel Machines	Introduction to Machine Learning, Ethem Alpaydın, Chapter 14
12	Hidden Markov Models	Introduction to Machine Learning, Ethem Alpaydın, Chapter 16
13	Combining Multiple Learners	Introduction to Machine Learning, Ethem Alpaydın, Chapter 18
14	Reinforcement Learning	Introduction to Machine Learning, Ethem Alpaydın, Chapter 19
15	Semester Review
16	Final Exam

Course Notes/Textbooks

Introduction to Machine Learning, Ethem Alpaydın, Fourth Edition, MIT Press, 9780262043793

Suggested Readings/Materials

Pattern Classification, Richard O. Duda and Peter E. Hart and David G. Stork, Second Edition, Wiley, 9780471056690

Pattern Recognition, Sergios Theodoridis and Konstantinos Koutroumbas, Fourth Edition, Academic Press, 9781597492720

Neural Networks and Learning Machines, Simon Haykin, Third Edition, Pearson, 9780131293762

Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 9780387310732

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	20
Presentation / Jury
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	1	25
Final Exam	1	35
Total

Weighting of Semester Activities on the Final Grade	4	100
Weighting of End-of-Semester Activities on the Final Grade
Total

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	14	5	70
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	1	35	35
Presentation / Jury			0
Project	1	30	30
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	22	22
		Total	225

#	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 Computer Engineering, evaluates, interprets and applies information.		-	-	X	-	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	X	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	-	X
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	X	-	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		X	-	-	-	-
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.		-	X	-	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		X	-	-	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		X	-	-	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		X	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		X	-	-	-	-

CE 531 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

EVALUATION SYSTEM

ECTS / WORKLOAD TABLE

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

NEW GÜZELBAHÇE CAMPUS

GLOBAL CAREER

CONTRIBUTION TO SCIENCE

VALUING PEOPLE

BENEFIT TO SOCIETY