Department of Computer Engineering

CE 345 | Course Introduction and Application Information

Course Name

Introduction to Machine Learning

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 345	SPRING	3	0	3	5

Prerequisites	None
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-to-Face
Teaching Methods and Techniques of the Course	Discussion Problem Solving Q&A Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Dr. Öğr. Üyesi Alper Demir
Course Lecturer(s)	Dr. Öğr. Üyesi Muhtar Çağkan Uludağlı
Assistant(s)	-

Course Objectives

Machine learning is about how to design computer programs that can automatically improve themselves with experience. The aim of this course is to review the latest and most effective algorithms used in the field of machine learning. Both theoretical properties and practical applications of these algorithms will be discussed.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Will be able to depict a wide range of many machine learning algorithms.					X
LO2	Will be able to apply basic techniques and algorithms in the field.					X
LO3	Will be able to compare different algorithms and techniques in the field.					X
LO4	Will be able to design machine learning algorithms for special situations.					X
LO5	Will be able to evaluate the potential areas of use of machine learning techniques.					X

Course Description

Machine learning deals with computer programs automatically improving their performance with past experience. The following topics will be covered in the machine learning course inspired by many fields such as artificial intelligence, statistics, information theory, biology and control theory; Discussion of computational learning theory, machine learning concepts, Bayesian learning, supervised learning, classification methods, regression methods, unsupervised learning, grouping methods, artificial neural networks, reinforcement learning and advanced machine learning methods.

Related Sustainable Development Goals

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction to Data Science with Python	Grus, Ch.s 2--6	72077c12
2	Introduction and Machine Learning Concepts	Alpaydın, Ch.1	7718f037
3	Bayesian Decision Theory and Bayesian Classification	Alpaydın, Ch.3	43aea17a
4	Supervised Learning - Parametric Classification Methods	Alpaydın, Ch.s 2, 10; Goodfellow et al, Ch. 5.5	ebe5f396
5	Supervised Learning - Nonparametric Classification Methods	Hastie et al, Ch. 13	7718f037
6	Supervised Learning - Regression Methods	Weisberg, Ch. 2	43aea17a
7	Machine Learning Metrics	Various academic articles	22bf79b6
8	Midterm		-
9	Unsupervised Learning - Clustering Methods	Alpaydın, Ch. 7; Geron, Ch. 9	43aea17a
10	Unsupervised Learning - Clustering Methods	Geron, Ch. 9; Murphy, Ch.s 25.3, 25.4, 25.5	72077c12
11	Unsupervised Learning - Artificial Neural Networks	Bishop, Ch. 5; Alpaydın, Ch. 11	7718f037
12	Unsupervised Learning - Artificial Neural Networks	Bishop, Ch. 5; Alpaydın, Ch. 11	ebe5f396
13	Reinforcement Learning	Alpaydın, Ch. 18	43aea17a
14	Reinforcement Learning and Advanced Machine Learning Methods	Alpaydın, Ch.s 11, 18; Goodfellow et al, Ch.s 6, 7, Murphy, Ch. 28	22bf79b6
15	Review of the semester		-
16	Final Exam		-

Course Notes/Textbooks

Alpaydın; E. (2014); Introduction to Machine Learning. The MIT Press; ISBN-13: 978-0-262-028189

Suggested Readings/Materials

Grus; J. (2019). Data science from scratch: first principles with python. O'Reilly Media; ISBN: 9781492041139
Murphy; K. P. (2012). Machine learning: a probabilistic perspective. MIT press; ISBN-13: 978-0262018029
Mitchell; T. M. (1997). Machine Learning. McGraw-Hill; ISBN: 0070428077
Bishop; C. M. (2006). Pattern recognition and machine learning. Springer; ISBN-13: 978-0387-31073-2
Hastie; T.; Tibshirani; R.; Friedman; J. H.; & Friedman; J. H. (2009). The elements of statistical learning: data mining; inference; and prediction. Springer; ISBN-13: 978-0-387-84857-0
Géron; A. (2022). Hands-on machine learning with Scikit-Learn; Keras; and TensorFlow. O'Reilly Media; Inc.; ISBN-13: 9781492032649
Weisberg; S. (2014). Applied linear regression. Wiley; ISBN-13: 9780471663799
Goodfellow; I.; Bengio; Y.; Courville; A. (2016). Deep learning. MIT Press; ISBN-13: 978-0262035613

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Quizzes / Studio Critiques	6	30	X	X	X	X	X
Midterm	1	30	X	X	X	X	X
Final Exam	1	40	X	X	X	X	X
Total	8	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	4	56
Field Work	-	-	-
Quizzes / Studio Critiques	6	2	12
Portfolio	-	-	-
Homework / Assignments	-	-	-
Presentation / Jury	-	-	-
Project	-	-	-
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	14	14
Final Exam	1	20	20
		Total	150

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
			1	2	3	4	5
No program competency data found.

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

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