Course  Machine Learning
Note: This course is offered by Stanford as an online course for credit. It can be taken individually, or as part of a masters degree or graduate certificate earned online through the Stanford Center for Professional Development. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/nonparametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Prerequisites: Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably nontrivial computer program; familiarity with basic probability theory; familiarity with basic linear algebra.

Lecture 1  The Motivation & Applications of Machine Learning

Lecture 2  An Application of Supervised Learning  Autonomous Deriving

Lecture 3  The Concept of Underfitting and Overfitting

Lecture 4  Newton's Method

Lecture 5  Discriminative Algorithms

Lecture 6  Multinomial Event Model

Lecture 7  Optimal Margin Classifier

Lecture 8  Kernels

Lecture 9  Bias variance Tradeoff

Lecture 10  Uniform Convergence  The Case of Infinite H

Lecture 11  Bayesian Statistics and Regularization

Lecture 12  The Concept of Unsupervised Learning

Lecture 13  Mixture of Gaussian

Lecture 14  The Factor Analysis Model

Lecture 15  Latent Semantic Indexing (LSI)

Lecture 16  Applications of Reinforcement Learning

Lecture 17  Generalization to Continuous States

Lecture 18  Stateaction Rewards

Lecture 19  Advice for Applying Machine Learning

Lecture 20  Partially Observable MDPs (POMDPs)