Information Technologies (English) (Thesis)
Master	Length of the Programme: 2	Number of Credits: 120	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF: Level 7

Ders Genel Tanıtım Bilgileri

School/Faculty/Institute

Graduate School

Course Code

ITC 502

Course Title in English

Machine Learning and Deep Learning

Course Title in Turkish

Machine Learning and Deep Learning

Language of Instruction

Type of Course

Flipped Classroom

Level of Course

Intermediate

Semester

Spring

Contact Hours per Week

Lecture: 3

Recitation: 0

Lab: 0

Other: 0

Estimated Student Workload

186 hours per semester

Number of Credits

7.5 ECTS

Grading Mode

Standard Letter Grade

Pre-requisites

None

Expected Prior Knowledge

None

Co-requisites

None

Registration Restrictions

Only Graduate 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. Topics include supervised learning, unsupervised learning, classification, and regression methods.

Course Description in Turkish

Bu ders yapay öğrenmede kullanılan temel yöntemleri içermektedir. Konular, gözetimli ve gözetimsiz öğrenme yöntemleri, sınıflandırma ve regresyon metodlarıdır.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) Apply classification methods to recognize patterns
2) Apply regression methods to estimate unknown functions
3) Assess the performance of machine learning methods
4) Design and construct a machine learning system for a given problem

Program Learning Outcomes/Course Learning Outcomes	1	2	3	4
1) An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications.
2) An ability to apply scientific and practical knowledge in statistics, computing and computer science.
3) A Comprehensive knowledge of analysis and modeling methods and their limitations.
4) An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process.
5) An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments.
6) An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities.
7) An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility.
8) An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context.
9) An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR.
10) An understanding the social and environmental aspects of IT applications.

Relation to Program Outcomes and Competences

N None	S Supportive	H Highly Related

	Program Outcomes and Competences	Level	Assessed by
1)	An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications.	N
2)	An ability to apply scientific and practical knowledge in statistics, computing and computer science.	N
3)	A Comprehensive knowledge of analysis and modeling methods and their limitations.	N
4)	An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process.	N
5)	An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments.	N
6)	An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities.	N
7)	An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility.	N
8)	An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context.	N
9)	An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR.	N
10)	An understanding the social and environmental aspects of IT applications.	N

Prepared by and Date	,
Course Coordinator	TUNA ÇAKAR
Semester	Spring
Name of Instructor	Asst. Prof. Dr. TUNA ÇAKAR

Course Contents

Week	Subject
1)	Introduction to Machine Learning Concepts
2)	Supervised and Unsupervised Methods
3)	Classification and Regression
4)	k-Nearest Neighbor
5)	Decision Trees
6)	Feature Selection
7)	Feature Extraction
8)	Clustering
9)	Term Project Progress Presentations
10)	Performance Evaluation: Training, Testing and Validation
11)	Artificial Neural Networks – Part 1
12)	Artificial Neural Networks – Part 1
13)	Deep Neural Networks
14)	Term Project Presentations
15)	Final Examination Period
16)	Final Examination 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
Quiz(zes)	2	% 40
Homework Assignments	3	% 60
TOTAL		% 100

Course Administration

Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54.

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	2	3	2	98
Project	4	2	18	2	88
Total Workload					186
Total Workload/25					7.4
ECTS					7.5