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Department of Computer Engineering
CE 475 | Course Introduction and Application Information
| Course Name |
Fundamentals and Applications of Machine Learning
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
CE 475
|
SPRING
|
2
|
2
|
3
|
7
|
| Prerequisites | MATH 240 To succeed (To get a grade of at least DD) | |||||
| Course Language | English | |||||
| Course Type | ELECTIVE_COURSE | |||||
| Course Level | First Cycle | |||||
| Mode of Delivery | ||||||
| Teaching Methods and Techniques of the Course |
Discussion Problem Solving Q&A Critical feedback Application: Experiment / Laboratory / Workshop Lecture / Presentation |
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| National Occupational Classification Code | - | |||||
| Course Coordinator |
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| Course Lecturer(s) |
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| Assistant(s) |
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| Course Objectives | This course provides a statistical foundation for machine learning and introduces students to machine learning based on this foundation. Students learn to apply machine learning algorithms to practical problems, select appropriate algorithms using statistical analysis methods, and evaluate the accuracy of the models they create. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning Outcomes |
The students who succeeded in this course;
|
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| Course Description | Fundamentals of linear algebra and probability, linear regression, nonlinear models, cross-range, model selection, decision trees, and support vector machines. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related Sustainable Development Goals |
-
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|
Core Courses |
|
| Major Area Courses |
X
|
|
| Supportive Courses |
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| Media and Managment Skills Courses |
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|
| Transferable Skill Courses |
|
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Required Materials | Learning Outcome |
| 1 | Introduction to Machine Learning | ISLR Ch.1 | LO1 |
| 2 | Conditional Probability and Linear Algebra review | Statistics for Engineers and Scientists by William Navidi, McGraw-Hill Education, 5th Edition, 2019. ISBN- 13: 978-1259717604 Ch. 2 | LO1 |
| 3 | Simple Linear Regression | ISLR Ch.3 | LO1 |
| 4 | Multiple Regression | ISLR Ch.3 | LO2 |
| 5 | Multiple Regression | ISLR Ch.3 | LO3 |
| 6 | Cross validation and bootstrapping | ISLR Ch.5 | LO4 |
| 7 | Model Selection | ISLR Ch.6 | LO5 |
| 8 | Nonlinear models | ISLR Ch.7 | LO2 |
| 9 | Decision Trees | ISLR Ch.8 | LO5 |
| 10 | Classification | ISLR Ch.4 | LO4 |
| 11 | Support Vector Machines | ISLR Ch.9 | LO1 |
| 12 | Principal Component Analysis | ISLR Ch.10 | LO3 |
| 13 | Clustering | ISLR Ch.10 | LO3 |
| 14 | Project Discussions and Presentations | LO5 | |
| 15 | Review of the Semester | - | |
| 16 | Final Exam | - |
| Course Notes/Textbooks | An Introduction to Statistical Learning: with Applications in R by Gareth James Daniela Witten Trevor Hastie Robert Tibshirani published by Springer ISBN-13: 978-1461471370 |
| Suggested Readings/Materials | Course Home Page |
EVALUATION SYSTEM
| Semester Activities | Number | Weighting | LO1 | LO2 | LO3 | LO4 | LO5 |
| Laboratory / Application | 1 | 24 | X | X | X | ||
| Quizzes / Studio Critiques | 2 | 8 | X | X | X | X | |
| Project | 1 | 26 | X | X | X | ||
| Final Exam | 1 | 42 | X | X | X | X | |
| Total | 5 | 100 |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Participation | - | - | - |
| Theoretical Course Hours | 16 | 2 | 32 |
| Laboratory / Application Hours | 16 | 2 | 32 |
| Study Hours Out of Class | 14 | 3 | 42 |
| Field Work | - | - | - |
| Quizzes / Studio Critiques | 4 | 4 | 16 |
| Portfolio | - | - | - |
| Homework / Assignments | - | - | - |
| Presentation / Jury | - | - | - |
| Project | 1 | 60 | 60 |
| Seminar / Workshop | - | - | - |
| Oral Exams | - | - | - |
| Midterms | - | - | - |
| Final Exam | 1 | 28 | 28 |
| Total | 210 |
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
| # | PC Sub | Program Competencies/Outcomes | * Contribution Level | ||||
| 1 | 2 | 3 | 4 | 5 | |||
| No program competency data found. | |||||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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