FACULTY OF ENGINEERING
Department of Software 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
|
Fall/Spring
|
2
|
2
|
3
|
7
|
Prerequisites |
|
|||||||
Course Language |
English
|
|||||||
Course Type |
Elective
|
|||||||
Course Level |
First Cycle
|
|||||||
Mode of Delivery | - | |||||||
Teaching Methods and Techniques of the Course | DiscussionProblem SolvingQ&ACritical feedbackApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||||
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. |
Learning Outcomes |
The students who succeeded in this course;
|
Course Description | Fundamentals of probabilistic reasoning and linear algebra, linear regression, nonlinear models, cross validation and bootstrapping, model selection, decision trees, and support vector machines. |
|
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 | ISLR Ch.1 |
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 |
3 | Simple Linear Regression | ISLR Ch.3 |
4 | Multiple Regression | ISLR Ch.3 |
5 | Multiple Regression | ISLR Ch.3 |
6 | Cross validation and bootstrapping | ISLR Ch.5 |
7 | Model Selection | ISLR Ch.6 |
8 | Nonlinear models | ISLR Ch.7 |
9 | Decision Trees | ISLR Ch.8 |
10 | Classification | ISLR Ch.4 |
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 | |
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 |
EVALUATION SYSTEM
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application |
1
|
24
|
Field Work | ||
Quizzes / Studio Critiques |
4
|
8
|
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | ||
Project |
1
|
26
|
Seminar / Workshop | ||
Oral Exams | ||
Midterm | ||
Final Exam |
1
|
42
|
Total |
Weighting of Semester Activities on the Final Grade |
6
|
58
|
Weighting of End-of-Semester Activities on the Final Grade |
1
|
42
|
Total |
ECTS / WORKLOAD TABLE
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
2
|
32
|
Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
2
|
32
|
Study Hours Out of Class |
14
|
3
|
42
|
Field Work |
0
|
||
Quizzes / Studio Critiques |
4
|
4
|
16
|
Portfolio |
0
|
||
Homework / Assignments |
0
|
||
Presentation / Jury |
0
|
||
Project |
1
|
60
|
60
|
Seminar / Workshop |
0
|
||
Oral Exam |
0
|
||
Midterms |
0
|
||
Final Exam |
1
|
28
|
28
|
Total |
210
|
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. |
|||||
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 multi-disciplinary 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. |
|||||
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 Software 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 Software Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest