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): 31

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: EN
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
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):
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
1) Knows the theories, different approaches and perspectives related to psychology and knows the basic concepts and terms related to psychology.
2) Knows the process of psychology becoming a science from a historical perspective, its approach to emotional, behavioral and mental processes in this process and the scientific methods used to investigate these processes.
3) Knows the working subjects and methods of the subfields of psychology and defines the duties of psychologists working in these fields.
4) To be able to deal with the human being as a biological, psychological, sociological and cultural whole and to be able to establish a relationship with related disciplines in this sense.
5) Knows the salient emphases of the major theories, approaches and perspectives of psychology (behavioral, biological, cognitive, evolutionary, humanistic, psychodynamic and sociocultural).
6) Knows the basic principles and methods of psychological assessment and gains basic interview skills.
7) Have a basic level of knowledge about commonly used measurement tools in related subfields of psychology.
8) To have the knowledge and skills required to conduct research in the field of psychology, to find a problem, to create a research design, to collect data, to determine appropriate data analysis, to interpret and report findings.
9) To have a good understanding of scientific thinking in psychology; to use critical thinking and reasoning skills.
10) Knows the ethical principles, values and practices of psychology.

Ders - Öğrenme Kazanımı İlişkisi

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Knows the theories, different approaches and perspectives related to psychology and knows the basic concepts and terms related to psychology.
2) Knows the process of psychology becoming a science from a historical perspective, its approach to emotional, behavioral and mental processes in this process and the scientific methods used to investigate these processes.
3) Knows the working subjects and methods of the subfields of psychology and defines the duties of psychologists working in these fields.
4) To be able to deal with the human being as a biological, psychological, sociological and cultural whole and to be able to establish a relationship with related disciplines in this sense.
5) Knows the salient emphases of the major theories, approaches and perspectives of psychology (behavioral, biological, cognitive, evolutionary, humanistic, psychodynamic and sociocultural).
6) Knows the basic principles and methods of psychological assessment and gains basic interview skills.
7) Have a basic level of knowledge about commonly used measurement tools in related subfields of psychology.
8) To have the knowledge and skills required to conduct research in the field of psychology, to find a problem, to create a research design, to collect data, to determine appropriate data analysis, to interpret and report findings.
9) To have a good understanding of scientific thinking in psychology; to use critical thinking and reasoning skills.
10) Knows the ethical principles, values and practices of psychology.

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 %

İş Yükü ve AKTS Kredisi Hesaplaması

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Project 1 40 40
Homework Assignments 7 5 35
Final 1 20 20
Total Workload 137