Information Systems Engineering | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | FET404 | ||||||||
Ders İsmi: | Artificial Intelligence Mathematics | ||||||||
Ders Yarıyılı: | Fall | ||||||||
Ders Kredileri: |
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Language of instruction: | Turkish | ||||||||
Ders Koşulu: | |||||||||
Ders İş Deneyimini Gerektiriyor mu?: | Yes | ||||||||
Type of course: | Bölüm Seçmeli | ||||||||
Course Level: |
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Mode of Delivery: | E-Learning | ||||||||
Course Coordinator : | Asst. Prof. Dr. SEDA KARATEKE | ||||||||
Course Lecturer(s): |
Asst. Prof. Dr. TAYMAZ AKAN |
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Course Assistants: |
Course Objectives: | This course will highlight the need for mathematical concepts by pointing directly to their usefulness in the context of basic Artificial Intelligence learning problems, and by emphasizing the mathematical foundations of basic Artificial Intelligence concepts; It aims to narrow the skill gap or even close it completely by gathering information in one place. |
Course Content: | Matrices and Systems of Linear Equations and their solutions, Gradient, local/spherical maximum and minimum, saddle point, convex functions, gradient descent algorithms - batch, mini-stack, stochastic, performance comparisons, Classical and convex optimization, Central Machine Learning Problems/Data analysis and Models, Central Machine Learning Problems/Linear Regression, Numerical computation, Boolean Algebra and Decision Trees, Algorithms and Statistics, Artificial Intelligence Case Studies. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | Introduction and Motivation | Lecture Notes |
2) | Mathematics Fundamentals/Linear Algebra > Matrices and Systems of Linear Equations and their solutions | Lecture Notes |
3) | Mathematics Fundamentals/Linear Algebra> Vector Spaces and Linear Independence | Lecture Notes |
4) | Mathematics Fundamentals/Matrix Decompositions>Determinants, Eigenvalues and Eigenvectors | Lecture Notes |
5) | Gradient, local/spherical maximum and minimum, saddle point, convex functions, gradient descent algorithms - batch, mini-stack, stochastic, performance comparisons | Lecture Notes |
6) | Vector Analysis | Lecture Notes |
7) | Probability and Distributions | Lecture Notes |
8) | Mid term | Lecture Notes |
9) | Classical and convex optimization | Lecture Notes |
10) | Center Machine Learning Problems/Data analysis and Models | Lecture Notes |
11) | Central Machine Learning Problems/Linear Regression | Lecture Notes |
12) | Numerical calculation | Lecture Notes |
13) | Boolean Algebra and Decision Trees | Lecture Notes |
14) | Algorithms and Statistics | Lecture Notes |
15) | Final | Lecture Notes |
Course Notes / Textbooks: | Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, Mathematics for Machine Learning. Dana H. Ballard, “An Introduction to Natural Computation”, Third Edition, MIT Press, 2000. |
References: | 1. https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 2. https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/lecture-notes/MIT18_657F15_LecNote.pdf 3. https://ichi.pro/tr/ai-ve-matematik-116112807338198 4. https://towardsdatascience.com/mathematics-for-ai-all-the-essential-math-topics-you-need-ed1d9c910baf 5. S. Haykin, "Neural Networks - A Comprehensive Foundation", Second Edition,Prentice Hall, 1999. |
Ders Öğrenme Kazanımları | 1 |
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