Computer Engineering (Master) (without Thesis) (English)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code:

AO5006

Course Name:

Aspects of Deep Learning

Semester:

Fall
Spring

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Dr. Öğr. Üy. HANDAN KULAN

Course Lecturer(s):

Dr.Handan Kulan

Course Assistants:

Course Objective and Content

Course Objectives:	The aim of this online course is to explain the principles of deep learning and its methods to students in theory and practice.
Course Content:	Students must do projects using Python. Projects will be done on a team basis.

Learning Outcomes

The students who have succeeded in this course;
1) Artificial Intelligence, Machine Learning and Deep learning concepts are defined
2) Data preprocessing steps are explained
3) Classification of machine learning algorithms and their algorithmic structure are explained.
4) Artificial intelligence model performance metrics explained
5) Artificial intelligence model codes are explained through Python language

Course Flow Plan

Week	Subject	Related Preparation
1)	What is AI, Machine Learning and Deep Learning?
2)	Phyton Programming Practices
3)	Phyton Programming Language Practices
4)	Introduction to Machine Learning
5)	Classification Process and Model Performance
6)	Data Preprocessing
7)	Clustering
8)	Midterm
9)	Artificial Neural Network
10)	Artificial Neural Network - Backpropogation
11)	Keras Paketi Kullanarak Derin Öğrenme
12)	Recurrent Neural Network, Long Short-Term Memory Neural Network
13)	Deep Autoencoder
14)	Convolutional Neural Networks

Sources

Course Notes / Textbooks:	Uygulamalı Derin Öğrenme (Yalçın Özkan)
References:	Uygulamalı Derin Öğrenme (Yalçın Özkan)

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
4) To be able to solve the problems encountered in the field by using research methods.
5) Being able to independently carry out a work that requires expertise in the field.
6) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
7) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
8) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
9) To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
10) To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field.
11) To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
12) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2)	To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3)	To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
4)	To be able to solve the problems encountered in the field by using research methods.
5)	Being able to independently carry out a work that requires expertise in the field.
6)	To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
7)	To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
8)	To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
9)	To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
10)	To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field.
11)	To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
12)	To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 70
Midterms	1	% 30
total		% 100
PERCENTAGE OF SEMESTER WORK		% 100
PERCENTAGE OF FINAL WORK		%
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	14	42
Presentations / Seminar	14	42
Midterms	14	28
Final	14	41
Total Workload		153