## SEE: Guide to Download Stanford Video Lecture

## Course Description :

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).

## Other Resources :

### Handouts | Citation |

## Other Computer Science Courses

- Biometrics by IIT Kanpur
- Operating Systems and System Programming,Fall 2011 by UC Berkeley
- Design and Analysis of Algorithms by IIT Bombay
- Numerical Optimization by IISc Bangalore
- CSCI E-52 Intensive Introduction to Computer Science Using C, PHP, and JavaScript by Harvard
- CSE142: Computer Programming I by University of Washington
- Numerical Methods and Programing by IIT Madras
- Introduction to Algorithms by MIT
- CSEP 590B Computing for the Developing World by University of Washington
- Advanced Multimedia by The University of New South Wales

### » check out the complete list of Computer Science lectures