Lecture 14 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)

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Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/

To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-autumn2018.html

0:00 Introduction
1:15 Unsupervised learning
1:38 First unsupervised learning algorithm
1:54 Market Segmentation
5:33 Clustering algorithm
5:37 K-means clustering
5:52 Initialize the cluster centroids
12:10 Cost function
16:32 Density Estimation
18:01 Anomaly Detection
20:40 Mixture of Gaussians Volatile
29:27 Maximum Likelihood Estimates
31:44 Bayes Rule
48:12 Jensen's Inequality
57:57 Density Estimation Problem
59:32 Maximum Likelihood Estimation
1:07:16 Concave form of Jensen's Inequality

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