Akademik Dersler - Tanıtım


Code - Course Title	EEM510 - Machine Learning for Dynamical Systems and Control
Course Type	Elective Courses
Language of Instruction	İngilizce
Laboratory + Practice	3+0
ECTS	7.5
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ ALTAN ONAT
Mode of Delivery	Face to Face
Prerequisites	None.
Courses Recomended	None.
Required or Recommended Resources	1) Brunton, Steven L., and J. Nathan Kutz. Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, 2022.
Recommended Reading List	All textbooks covering machine learning, dynamical systems, and control systems.
Assessment methods and criteria	Homeworks and Final Project
Work Placement	None.
Sustainability Development Goals

Weeks	Topics
Week - 1	Fundamental Concepts: Regression and Model Selection, Clustering and Classification an Overview
Week - 2	Fundamental Concepts: Neural Networks an Overview
Week - 3	Fundamental Concepts: Deep Learning an Overview
Week - 4	Data-Driven Dynamical Systems: Overview
Week - 5	Data-Driven Dynamical Systems: Dynamic Mode Decomposition
Week - 6	Data-Driven Dynamical Systems: Sparse Identification of Nonlinear Dynamics
Week - 7	Data-Driven Dynamical Systems: Koopman Operator Theory
Week - 8	Data-Driven Dynamical Systems: Data Driven Koopman Analysis
Week - 9	Data Driven Control: Model Predictive Control
Week - 10	Data Driven Control: Nonlinear System Identification for Control
Week - 11	Data Driven Control: Machine Learning Control
Week - 12	Physics Informed Machine Learning: SINDY Autoencoder-Coordinates and Dynamics
Week - 13	Physics Informed Machine Learning: Koopman Forecasting
Week - 14	Physics Informed Machine Learning: Physics Informed Machine Learning

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Homework	1	60
Final Exam	1	40
Toplam (%)		100