FACULTY OF ENGINEERING
Department of Software Engineering
CE 395 | Course Introduction and Application Information
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
|
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Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | Problem SolvingLecture / Presentation | |||||
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. |
Learning Outcomes |
The students who succeeded in this course;
|
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. |
|
Core Courses | |
Major Area Courses | ||
Supportive Courses | ||
Media and Management Skills Courses | ||
Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
Week | Subjects | Related Preparation |
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. |
EVALUATION SYSTEM
Semester Activities | Number | Weigthing |
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 |
ECTS / WORKLOAD TABLE
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
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
#
|
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. |
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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. |
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6 | To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually. |
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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. |
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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. |
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9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. |
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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. |
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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. ("European Language Portfolio Global Scale", Level B1) |
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12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
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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. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest