Introduction to Aa 17 18 Lecture 13
Exploring Aa 17 18 Lecture 13 reveals several interesting facts. Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering.
Aa 17 18 Lecture 13 Comprehensive Overview
Introduction to clustering. K-means and k-medoids. Expectation maximization. This Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering. Bayesian ...
Supervised learning, minimization (least squares), polynomial regression.
Summary & Highlights for Aa 17 18 Lecture 13
- Introduction.
- Lecture
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
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