PSYCHOLOGY (ENGLISH) | |||||
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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 |
Course Code: | 5010003113 | ||||||||||
Ders İsmi: | Artificial intelligence | ||||||||||
Ders Yarıyılı: | Fall | ||||||||||
Ders Kredileri: |
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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: |
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Mode of Delivery: | E-Learning | ||||||||||
Course Coordinator : | Prof. Dr. Haluk GÜMÜŞKAYA | ||||||||||
Course Lecturer(s): |
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Course Assistants: |
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. |
The students who have succeeded in this course;
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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 |
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 Öğrenme Kazanımları | |||||||||
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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. |
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. |
Semester Requirements | Number of Activities | Level of Contribution |
total | % | |
PERCENTAGE OF SEMESTER WORK | % 0 | |
PERCENTAGE OF FINAL WORK | % | |
total | % |
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 |