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About Tutorial Part I Information Theory Meets Machine Learning Tuto - Slides - Part1
This document provides an introduction to a tutorial on the intersection of information theory and machine learning. It discusses several key issues at this intersection, including the concentration of measure in high dimensions, the curse of dimensionality without structure, and the importance of low-dimensional structure. It also notes the importance of algorithmic and computational constraints in machine learning. The tutorial will present three examples exploring graphical model selection, sparse principal component analysis, and structured non-parametric regression.
Detailed Information
| Author: | ['apdpjp'] |
|---|---|
| Publication Year: | 2016 |
| Pages: | 46 |
| Language: | English |
| Format: | |
| Price: | FREE |
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