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
Fall/Spring
2
2
3
7

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Course Coordinator
Course Lecturer(s) -
Assistant(s) -
Course Objectives This course provides a statistical foundation for machine learning, and introduces the students to machine learning algorithms based on this foundation. Students learn to apply such algorithms to practical problems and utilize the statistical insights to select appropriate algorithms, and interpret the accuracy of resulting models.
Course Description The students who succeeded in this course;
  • will be able to define supervised learning versus unsupervised learning.
  • will be able to list basic machine learning models.
  • will be able to build different types of models for a range of applications, using available programming tools.
  • will be able to measure model accuracy and flexibility using statistical methods.
  • will be able to refine machine learning models by utilizing accuracy and flexibility analysis.
Course Content Fundamentals of probabilistic reasoning and linear algebra, linear regression, nonlinear models, cross validation and bootstrapping, model selection, decision trees, and support vector machines.

 



Course Category

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 to Machine Learning Instructor Notes
2 Conditional Probability and Linear Algebra review Instructor Notes
3 Simple Linear Regression ISLR Ch.3
4 Multiple Regression ISLR Ch.3
5 Multiple Regression ISLR Ch.3
6 Classification ISLR Ch.4
7 Cross validation and bootstrapping ISLR Ch.5
8 Model Selection ISLR Ch.6
9 Nonlinear models ISLR Ch.7
10 Decision Trees ISLR Ch.8
11 Support Vector Machines ISLR Ch.9
12 Principal Component Analysis ISLR Ch.10
13 Clustering ISLR Ch.10
14 Project Discussions and Presentations
15 Review of the Semester Instructor Notes
16 Review of the Semester Instructor Notes

 

Course Notes/Textbooks

An Introduction to Statistical Learning: with Applications in R, by  Gareth JamesDaniela WittenTrevor HastieRobert Tibshirani published by Springer ISBN-13: 978-1461471370

Suggested Readings/Materials

Machine Learning in Python by Michael Bowles, Wiley Publishing ISBN: 978-1-118-96174-2

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
8
20
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
Project
1
40
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
40
Final / Oral Exam
Total

Weighting of Semester Activities on the Final Grade
10
100
Weighting of End-of-Semester Activities on the Final Grade
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
Including exam week: 16 x total hours
16
2
32
Laboratory / Application Hours
Including exam week: 16 x total hours
16
2
Study Hours Out of Class
16
4
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
Project
1
52
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
20
Final / Oral Exam
    Total
200

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

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