PSYCHOLOGY (ENGLISH) | |||||
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Qualification Awarded | Program Süresi | Toplam Kredi (AKTS) | Öğretim Şekli | Yeterliliğin Düzeyi ve Öğrenme Alanı | |
4 | 240 | FULL TIME |
TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 6 QF-EHEA:First Cycle TR-NQF-HE, ISCED (1997-2013): |
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): |
Prof. Dr. Haluk GÜMÜŞKAYA |
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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 |
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PERCENTAGE OF SEMESTER WORK | % 0 | |
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