CE 395 | Course Introduction and Application Information - Department of Software Engineering

Course Name

Special Topics in Machine Learning

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 395	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	Problem Solving Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Kaya OĞUZ
Course Lecturer(s)	-
Assistant(s)	-

Course Objectives

The course covers key background topics from advanced machine learning including sampling and information theory, digital filtering and discrete Fourier transform, basics of vector and matrix manipulations, numerical optimization, and the fundamentals of the theory of statistical learning.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to apply principles of signal sampling and filtering in data processing algorithms,
2	will be able to use vector and matrix quantities in data processing algorithms,
3	will be able to use numerical optimization methods in machine learning algorithms,
4	will be able to describe the main principles of the theory of statistical learning,
5	will be able to contrast machine learning algorithms wıth respect to bias-variance trade-off, model complexity, and cross-validation error.

Course Description

The following topics will be included: sampling and information theory, digital filters and discrete Fourier transform, basics of vector and matrix manipulations, basics of numerical optimization, principles of statistical 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: What is Machine Learning?	Chapter 1. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning. ISBN 9780387216065
2	Basics of signal sampling - sampling rate, Nyquist frequency, resolution of signals and images, Shannon information, efficient codes, data compression	Chapter 1. Signals & Systems. Oppenheim & Willsky. ISBN 0136511759.
3	Introduction to digital filters, convolution, LTI theory, 1D and 2D filters, linear and nonlinear filters	Chapter 2. Signals & Systems. Oppenheim & Willsky. ISBN 0136511759.
4	Fourier transform, discrete Fourier transform, spectrum of signals, spectrum of images, complex numbers	Chapter 3. Signals & Systems. Oppenheim & Willsky. ISBN 0136511759.
5	Basics of linear algebra, row and column vectors, matrices, matrix multiplication, outer multiplication, norm	Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition
6	Basics of numerical optimization, optimality conditions, KKT conditions, gradient descent, convex optimization programs	Chapter 1. Sections 1.1-1.4, Chapter 4. Sections 4.3, 4.4. Nonlinear Programming, D. Bertsekas, Athena Scientific, 3rd Edition
7	Midterm exam
8	Primal-dual theory, large scale optimization, stochastic gradient descent	Chapter 2. Chapter 6. Sections 6.1-6.4. Nonlinear Programming, D. Bertsekas, Athena Scientific, 3rd Edition
9	Review of probability, random variables, probability distributions, Bayes theorem, expectation values, LLN, CLT, Markov, Jensen, Chernoff and Hoeffding inequalities	Statistics for Engineers and Scientists, William Navidi, 4th Ed., Mc-Graw Hill.
10	Introduction to statistical learning theory - learning as statistical activity, supervised and unsupervised learning, regression and classification	Chapter 2. Sections 2.1-2.3. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065
11	Statistical decision theory, function estimation, statistical models, restricted estimators, dimensionality curse, bias-variance trade-off	Chapter 2. Sections 2.4-2.6, 2.8, Chapter 7. Section 7.2. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065
12	Model assessment and selection, effective model dimension, AIC, BIC, Vapnik-Chervonenkis dimensions	Chapter 7. Sections 7.2-7.7. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065
13	Vapnik-Chervonenkis dimensions, cross-validation and why it works, bootstrap methods	Chapter 7. Sections 7.9-7.11. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065
14	General semester review
15	General semester review
16	General semester review

Course Notes/Textbooks

A. Oppenheim, A. Willsky, Signals & Systems, Pearson, 1996, ISBN 0136511759

Suggested Readings/Materials

D. Lay, S. Lay, J. McDonald, Linear Algebra and Its Applications, Pearson, 5th Edition, 2015, ISBN 9780321982384

D. Bertsekas, Nonlinear Programming, Athena Scientific, 3rd Edition, 2016, ISBN 9781886529052

W. Navidi, Statistics for Engineers and Scientists, Mc-Graw Hill, 3rd Edition, 2010, ISBN 9780073376332

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, 2013, ISBN 9780387216065.

Semester Activities	Number	Weigthing	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	5	20
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	50
Total

Weighting of Semester Activities on the Final Grade	6	50
Weighting of End-of-Semester Activities on the Final Grade	1	50
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	2	28
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	5	6	30
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	24	24
		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		-	-	-	-	X
	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.		-	-	-	X	-
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		-	-	X	-	-
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 395 | Course Introduction and Application Information

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