Machine Learning

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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).

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