PSYCHOLOGY (ENGLISH)
Qualification Awarded	Program Süresi	Toplam Kredi (AKTS)	Öğretim Şekli	Yeterliliğin Düzeyi ve Öğrenme Alanı
Bachelor's (First Cycle) Degree	4	240	FULL TIME	TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 6 QF-EHEA:First Cycle TR-NQF-HE, ISCED (1997-2013):

Ders Genel Tanıtım Bilgileri

Course Code:

5010003113

Ders İsmi:

Artificial intelligence

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Labs	Credit	ECTS
2	0	0	2	3

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Other Recommended Topics for the Course:

Type of course:

Üniversite Seçmeli

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator :

Prof. Dr. Haluk GÜMÜŞKAYA

Course Lecturer(s):

Prof. Dr. Haluk GÜMÜŞKAYA

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

This course provides an introduction to Artificial Intelligence for public using modern tools and techniques.

Course Content:

Introduction to AI, Machine Learning and Deep Learning, Basic Data Science Toolset, Getting to Know Your Data, Data Preprocessing and Feature Engineering, Classification: Decision Trees and Ensembly Learning, Regression: Regression with One Variable, Regression: Regression with Multiple Input Variables, Polynomical Regression, Basic Techniques of Machine Learning, Regularized Linear Models, Classification: Logistic Regression, Introduction to Neural Networks and Deep Learning, AI Applications, Ethical Issues in AI.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction to AI, Machine Learning and Deep Learning
2)	Basic Data Science Toolset, Getting to Know Your Data
3)	Data Preprocessing and Feature Engineering
4)	Classification: Decision Trees and Ensembly Learning
5)	Regression: Regression with One Variable
6)	Regression: Regression with Multiple Input Variables
7)	Polynomial Regression
8)	Midterm Exam
9)	Basic Techniques of Machine Learning
10)	Classification: Logistic Regression
11)	Introduction to Neural Networks - 1
12)	Introduction to Neural Networks - 2
13)	Introduction to Deep Learning
14)	AI Applications, Ethical Issues in AI

Sources

Course Notes / Textbooks:	Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow (2022), 3rd Edition, Aurélien Geron
References:	Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow (2022), 3rd Edition, Aurélien Geron

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları
Program Outcomes

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

Program Outcomes

Level of Contribution

Öğrenme Etkinliği ve Öğretme Yöntemleri

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
total		%
PERCENTAGE OF SEMESTER WORK		% 0
PERCENTAGE OF FINAL WORK		%
total		%