Department of Statistics Graduate Student Seminar: Morteza Noshad, Department of Computer Science
Optimum Methods for Estimation of Information Measures.
Abstract:
In this talk we will introduce the information theoretic tools such as divergence and correlation measures that are useful in many machine learning algorithms. We will discuss different optimum methods proposed for estimation of these measures. In particular, the novel fast graph theoretic approaches used for estimation of the information measures will be discussed. In summary, in this presentation we make connections between information theory, graph theory and machine learning.
In this talk we will introduce the information theoretic tools such as divergence and correlation measures that are useful in many machine learning algorithms. We will discuss different optimum methods proposed for estimation of these measures. In particular, the novel fast graph theoretic approaches used for estimation of the information measures will be discussed. In summary, in this presentation we make connections between information theory, graph theory and machine learning.
| Building: | West Hall |
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| Website: | |
| Event Type: | Workshop / Seminar |
| Tags: | seminar |
| Source: | Happening @ Michigan from Department of Statistics Graduate Seminar Series, Department of Statistics |
