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
-
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
Course Coordinator
Course Lecturer(s) -
Assistant(s) -
Course Objectives
Learning Outcomes The students who succeeded in this course;
  • will be able to apply principles of signal sampling and filtering in data processing algorithms,
  • will be able to use vector and matrix quantities in data processing algorithms,
  • will be able to use numerical optimization methods in machine learning algorithms,
  • will be able to describe the main principles of the theory of statistical learning,
  • will be able to contrast machine learning algorithms wıth respect to bias-variance trade-off, model complexity, and cross-validation error.
Course Description
Related Sustainable Development Goals

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Review of the Semester  
2 Review of the Semester  
3 Review of the Semester  
4 Review of the Semester  
5 Review of the Semester  
6 Review of the Semester  
7 Review of the Semester  
8 Review of the Semester  
9 Review of the Semester  
10 Review of the Semester  
11 Review of the Semester  
12 Review of the Semester  
13 Review of the Semester  
14 Review of the Semester  
15 Review of the Semester  
16 Review of the Semester  
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 Weighting
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
20
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
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
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
28
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
6
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
1
20
Final Exams
1
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 and Computer 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 Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer 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 engineering problems or Computer Engineering research topics.

6

To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

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 present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of Computer 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 Computer Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized 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

To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

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 Computer Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest