CE 345 | Course Introduction and Application Information - Department of Software Engineering

Course Name

Introduction to Machine Learning

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

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Discussion Problem Solving Q&A Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Alper Demir
Course Lecturer(s)	Araş. Gör. Dr. M. Çağkan ULUDAĞLI
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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to distinguish between a range of machine learning techniques.
2	will be able to apply the basic techniques/algorithms of the field.
3	will be able to compare various techniques/algorithms of the field.
4	will be able to design various Machine Learning algorithms to specific situations.
5	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; computational learning theory, machine learning concepts, Bayesian learning, supervised learning, classification methods, regression methods, unsupervised learning, clustering methods, artificial neural networks, reinforcement learning, and discussion of advanced machine learning methods.

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 to Data Science with Python	Grus, Ch.s 2--6
2	Introduction and Machine Learning Concepts	Alpaydın, Ch.1
3	Bayesian Decision Theory and Classification	Alpaydın, Ch.3
4	Supervised Learning - Parametric Classification Methods	Alpaydın, Ch.s 2, 10; Goodfellow et al, Ch. 5.5
5	Supervised Learning - Non-parametric Classification Methods	Hastie et al, Ch. 13
6	Supervised Learning - Regression Methods	Weisberg, Ch. 2
7	Machine Learning Metrics	Various articles and studies
8	Midterm Exam
9	Unsupervised Learning - Clustering Methods	Alpaydın, Ch. 7; Geron, Ch. 9
10	Unsupervised Learning - Clustering Methods	Geron, Ch. 9; Murphy, Ch.s 25.3, 25.4, 25.5
11	Unsupervised Learning - Neural Networks	Bishop, Ch. 5; Alpaydın, Ch. 11; Hastie et al, Ch. 11
12	Unsupervised Learning - Neural Networks	Bishop, Ch. 5; Alpaydın, Ch. 11; Hastie et al, Ch. 11
13	Reinforcement Learning	Alpaydın, Ch. 18
14	Reinforcement Learning and Advanced Machine Learning Methods	Alpaydın, Ch.s 11, 18; Goodfellow et al, Ch.s 6, 7, Murphy, Ch. 28
15	Semester review
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

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	6	30
Portfolio
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	7	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
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	4	56
Field Work			0
Quizzes / Studio Critiques	6	2	12
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	14	14
Final Exam	1	20	20
		Total	150

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	-	-	-	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	-	-	-
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	-	-
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	-	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	-	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether face-to-face, remote, or hybrid).		-	-	-	-	-
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.		-	-	-	-	-
	1	Knowledge of business practices such as project management and economic feasibility analysis; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

CE 345 | Course Introduction and Application Information

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