Course Information

Course Name	Turkish	İşaret İşleme için Makine Öğrenmesi
	English	Machine Learning for Signal Processing
Course Code	EHB 328E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		English
Course Coordinator		Ibraheem Abdullah Mohammed Shayea
Course Objectives		(1) Introduce the student to the basic techniques of machine learning geared towards signal processing applications (2) Provide the students with non-trivial examples to develop their programming skills (3) Gain a working knowledge of probability, linear algebra, signal processing motivated by problems of current interest
Course Description		The course will include the following topics: Data-driven representations. Principal Component Analysis (PCA) and Kernel PCA. Independent Component Analysis (ICA). Non-negative matrix factorization (NMF). Dictionary based, sparse and overcomplete data representations. Low rank matrix representations. Regression and Linear prediction. Stochastic Gradient Descent and LMS adaptive filters. Clustering and Classification. Neural Networks. Convolutional networks and applications to signal and image processing. A good knowledge of probability theory, linear algebra and signals and systems theory is a prerequisite for the course. The term project and homework will necessitate software simulations.
Course Outcomes		Students who pass the course will be able to: (I) Propose a parametric model for a given signal processing learning problem and estimate the parameters from data, (II) Given a set of observations and classes, devise a system to decide which class the observations fall into, (III) Apply computationally feasible numerical algorithms for solving large scale regression problems, (IV) Reduce the size of data in order to realize a processing task more efficiently.
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