EEE 485 Statistical Learning and Data Analytics
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Introduction to the goals and tools of machine learning and data analytics. Overview of machine learning on diverse data acquired by: sensor networks, physiological devices, etc. Fundamental learning models. Applications: decision support, computer vision, recommender systems. Performance analysis by using probabilistic approach. Bayesian and frequentist machine learning. Classification and regression. Linear regression, Ridge regression, Lasso. Parameter estimation and Bayesian regression. Generalized linear models. Neural Networks. Learning from unlabeled data: probabilistic clustering, blind signal separation and feature extraction. Graphical models. Techniques for handling missing and corrupted data. Deep learning, transfer learning, online learning.
Credit units: 3 ECTS Credit units: 5, Prerequisite:
(MATH 255 or MATH 230 or MATH 260) and (MATH 241 or MATH 225 or MATH 220 or MATH 224) .
Autumn Semester (Süleyman Serdar Kozat)
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