Electrical and Electronics Engineering
Bachelor	Length of the Programme: 4	Number of Credits: 240	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF: Level 6

ECTS Course Information Package

School/Faculty/Institute

Faculty of Engineering

Course Code

COMP 462

Course Title in English

Introduction to Machine Learning

Course Title in Turkish

Yapay Öğrenmeye Giriş

Language of Instruction

Type of Course

Flipped Classroom

Level of Course

Introductory

Semester

Fall

Contact Hours per Week

Lecture: 3

Recitation: None

Lab: None

Other: None

Estimated Student Workload

152 hours per semester

Number of Credits

6 ECTS

Grading Mode

Standard Letter Grade

Pre-requisites

None

Co-requisites

None

Expected Prior Knowledge

Prior knowledge in programming, probability and statistics.

Registration Restrictions

Only undergraduate students

Overall Educational Objective

To learn the fundamentals of machine learning methods and how to design and implement intelligent systems to make prediction, classification, and regression.

Course Description

This course covers the fundamentals of machine learning approaches: Supervised learning, unsupervised learning, regression methods, outlier detection, feature analysis, validation and evaluation.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) Identify and solve a complex engineering problem using machine learning techniques;
2) Design a machine learning system to produce solutions;
3) Present the results of a machine learning solution to a range of audiences;
4) Recognize ethical and professional responsibilities in creating the machine learning system;
5) Analyze and interpret the data used for the machine learning system;
6) Acquire and apply new knowledge of machine learning techniques.

Program Learning Outcomes/Course Learning Outcomes	1	2	3	4	5	6
1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3) An ability to communicate effectively with a range of audiences
4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies

Relation to Program Outcomes and Competences

N None	S Supportive	H Highly Related

	Program Outcomes and Competences	Level	Assessed by
1)	An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics	H	Exam,HW,Project
2)	An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors	H	Exam,HW,Project
3)	An ability to communicate effectively with a range of audiences	S	HW,Project
4)	An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts	S	HW,Project
5)	An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives	N
6)	An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions	S	HW,Project
7)	An ability to acquire and apply new knowledge as needed, using appropriate learning strategies	S	HW,Project

Prepared by and Date	YASSINE DRIAS , February 2023
Course Coordinator	YASSINE DRIAS
Semester	Fall
Name of Instructor

Course Contents

Week	Subject
1)	Introduction to Machine Learning Concepts
2)	K Nearest Neighbor Algorithm
3)	Training, Testing and Validating Machine Learning Systems
4)	Clustering Techniques
5)	Decision Trees (Part 1)
6)	Decision Trees (Part 2)
7)	Gradient Descent Algorithm and Linear Regression
8)	Logistic Regression
9)	Feature Selection and Extraction Techniques
10)	Advanced Classification Methods
11)	Artificial Neural Networks (Part 1)
12)	Artificial Neural Networks (Part 2)
13)	Deep Learning Methods (Part 1)
14)	Deep Learning Methods (Part 2)
15)	Final Examination/Project/Presentation Period
16)	Final Examination/Project/Presentation Period

Required/Recommended Readings

Introduction to Machine Learning, Ethem Alpaydın, MIT Press, 3rd Edition (2015)

Teaching Methods

Flipped Classroom

Homework and Projects

Assignments

Laboratory Work

None

Computer Use

Required

Other Activities

None

Assessment Methods

Assessment Tools	Count	Weight
Homework Assignments	3	% 35
Project	1	% 30
Midterm(s)	1	% 35
TOTAL		% 100

Course Administration

driasy@mef.edu.tr

Instructor’s office: 5th floor Phone number: 0 212 395 37 45 Office hours: After the lecture hours. E-mail address: driasy@mef.edu.tr Rules for attendance: No attendance required. Statement on plagiarism: YÖK Regulations http://3fcampus.mef.edu.tr/uploads/cms/webadmin.mef.edu.tr/4833_2.pdf

ECTS Student Workload Estimation

Activity	No/Weeks	Hours			Calculation
	No/Weeks per Semester	Preparing for the Activity	Spent in the Activity Itself	Completing the Activity Requirements
Course Hours	14	1	3	1	70
Homework Assignments	4	1	14	2	68
Midterm(s)	1	10	2	2	14
Total Workload					152
Total Workload/25					6.1
ECTS					6