Course Information

Course Name	Turkish	Veri Beslemeli Kontrol
	English	Data Driven Control
Course Code	KON 447E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	7	3	3	-	-
Course Language		English
Course Coordinator		Kemal Uçak
Course Objectives		I. To teach the basic concepts of data-driven control systems. II. To provide the application of machine learning algorithms to control engineering problems. III. To provide students with data collection and analysis competence for system identification, state estimation and control design. IV. To teach data-driven modeling methods such as support vector machines and neural networks. V. To provide students with the ability to apply machine learning-based solutions in modern control systems.
Course Description		Traditional and data-driven control, singular value decomposition, sparsity and compressed sensing, regression, model selection, clustering and classification, support vector machines, neural networks and deep learning, extreme learning machines, data-driven dynamical systems, dynamic mode decomposition, sparse identification, koopman operator theory, data-driven koopman analysis, data-driven control
Course Outcomes		Upon successful completion of this course, students will be able to: I.Explain the fundamental concepts of data-driven control systems and their differences from traditional methods. II.Apply data analysis, dimensionality reduction, feature selection, regression, classification, and clustering techniques to control problems. III.Integrate neural networks, support vector machines, and deep learning models into control design and prediction tasks. IV.Develop data-driven control algorithms, design learning-based controllers, and evaluate their advantages and limitations.
Pre-requisite(s)		(EEF 281/E Linear algebra and Application(Min DD) AND EEF 206/E Signal Processing and Linear Systems(Min DD)) OR KON313/E Feedback Control Systems(Min DD)
Required Facilities
Other
Textbook		Steven L. Brunton and J. Nathan Kutz (2022) Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Second Edition, Cambridge University Press
Other References		Carlo Novara,Simone Formentin (2019) Data-Driven Modeling, Filtering and Control: Methods and Applications The Institution of Engineering and Technology Ali Khaki-Sedigh (2024) An Introduction to Data-Driven Control Systems. IEEE Press Wiley Ryan G. McClarren (2021) Machine Learning for Engineers: Using data to solve problems for physical systems. Springer Nello Cristianini, John Shawe-Taylor (2000) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press

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