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Machine Learning

Stanford, , Prof. Andrew Ng

Overview

Contents:
introduction,The Motivation Applications of Machine Learning - An Application of Supervised Learning - Autonomous Deriving - The Concept of Under fitting and Over fitting - Newtons Method - Discriminative Algorithms - Multinomial Event Model - Optimal Margin Classifier - Kernels - Bias/variance Trade off - Uniform Convergence - The Case of Infinite H - Bayesian Statistics and Regularization - The Concept of Unsupervised Learning - Mixture of Gaussian-The Factor Analysis Model - Latent Semantic Indexing (LSI) - Applications of Reinforcement Learning - Generalization to Continuous States - State-action Rewards - Advice for Applying Machine Learning - Partially Observable MDPs (POMDPs).

Includes