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


Course Coordinator  
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. 
Course Description 
The students who succeeded in this course;

Course Content  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. 

Core Courses  
Major Area Courses  
Supportive Courses  
Media and Management Skills Courses  
Transferable Skill Courses 
Week  Subjects  Related Preparation 
1  Introduction: What is Machine Learning?  
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 1.11.4, Chapter 4. Sections 4.3, 4.4. Nonlinear Programming, D. Bertsekas, Athena Scientific, 3rd Edition 
7  Midterm exam  
8  Primaldual theory, large scale optimization, stochastic gradient descent  Chapter 2. Chapter 6. Sections 6.16.4. 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., McGraw Hill. 
10  Introduction to statistical learning theory  learning as statistical activity, supervised and unsupervised learning, regression and classification  Chapter 2. Sections 2.12.3. 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, biasvariance tradeoff  Chapter 2. Sections 2.42.6, 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, VapnikChervonenkis dimensions  Chapter 7. Sections 7.27.7. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065 
13  VapnikChervonenkis dimensions, crossvalidation and why it works, bootstrap methods  Chapter 7. Sections 7.97.11. 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  Lecture notes 
Suggested Readings/Materials  A. Oppenheim, A. Willsky, Signals & Systems, Pearson, 1996, ISBN 0136511759; 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, McGraw 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 
Participation  
Laboratory / Application  
Field Work  
Quizzes / Studio Critiques  
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 EndofSemester 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


Study Hours Out of Class 
14

2


Field Work  
Quizzes / Studio Critiques  
Homework / Assignments 
5

6


Presentation / Jury  
Project  
Seminar / Workshop  
Oral Exam  
Midterms 
1

20


Final Exam 
1

24


Total 
150

#

Program Competencies/Outcomes 
* Contribution Level


1 
2 
3 
4 
5 

1  To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. 
X  
2  To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. 
X  
3  To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose. 
X  
4  To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively. 
X  
5  To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems. 
X  
6  To be able to work effectively in Software Engineering disciplinary and multidisciplinary teams; to be able to work individually. 
X  
7  To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able 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 and software applications 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 and Software Engineering solutions. 
X  
9  To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. 
X  
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. 
X  
11  To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language. 
X  
12  To be able to speak a second foreign language at a medium level of fluency efficiently. 

13  To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering. 
X 
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest