CE 395 | Course Introduction and Application Information - Department of Mechatronics 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	To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems		-	-	-	-	-
2	To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.		-	-	-	-	-
3	To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.		-	-	-	-	-
4	To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.		-	-	-	-	-
5	To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.		-	-	-	-	-
6	To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.		-	-	-	-	-
7	To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.		-	-	-	-	-
8	To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions.		-	-	-	-	-
9	To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications.		-	-	-	-	-
10	To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.		-	-	-	-	-
11	Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)		-	-	-	-	-
12	To be able to use the second foreign language at intermediate level.		-	-	-	-	-
13	To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.		-	-	-	-	-

CE 395 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

EVALUATION SYSTEM

ECTS / WORKLOAD TABLE

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