ECE 6254 - Statistical Machine Learning

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Description

This course will provide an introduction to the theory of statistical learning and practical machine learning algorithms with applications in signal processing and data analysis. In contrast to most traditional approaches to statistical inference and signal processing, in this course we will focus on how to learn effective models from data and how to apply these models to practical signal processing problems. We will approach these problems from the perspective of statistical inference. We will study both practical algorithms for statistical inference and theoretical aspects of how to reason about and work with probabilistic models. We will consider a variety of applications, including classification, prediction, regression, clustering, modeling, and data exploration/visualization.

Syllabus

Download syllabus PDF v2 Slides

Self assessment

Download the self assessment PDF

Class meeting times

  • Tuesdays 12pm-13:15pm in Klaus 1443
  • Thursdays 12pm-13:15pm in Klaus 1443

Prof. Bloch office hours

  • Wednesdays 12pm-1:15pm in TSRB 423 and BlueJeans - please indicate your presence for office hours
  • I plan to attend!

TAs office hours

  • Monday: Mehrdad (TSRB 523a) - 2:00pm-3:15pm
  • Tuesday: TJ (VL C449 Cubicle B) - 1:30pm - 2:45pm
  • Thursday: Hossein (VL C449 Cubicle D): 10:45pm - 12:00pm
  • Friday: Brighton (TSRB 523a) - 12pm-1:15pm