Latent Semantic Indexing (LSI)

Lecture Description

Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD) Implementation, Independent Component Analysis (ICA), The Application of ICA, Cumulative Distribution Function (CDF), ICA Algorithm, The Applications of ICA

Course Description

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric 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 non-trivial computer program; familiarity with basic probability theory; familiarity with basic linear algebra.

Related Resources

Transcript   |  CS 229 Notes 11

Course Index

1. The Motivation & Applications of Machine Learning
2. An Application of Supervised Learning - Autonomous Deriving
3. The Concept of Underfitting and Overfitting
4. Newton's Method
5. Discriminative Algorithms
6. Multinomial Event Model
7. Optimal Margin Classifier
8. Kernels
9. Bias/variance Tradeoff
10. Uniform Convergence - The Case of Infinite H
11. Bayesian Statistics and Regularization
12. The Concept of Unsupervised Learning
13. Mixture of Gaussian
14. The Factor Analysis Model
15. Latent Semantic Indexing (LSI)
16. Applications of Reinforcement Learning
17. Generalization to Continuous States
18. State-action Rewards
19. Advice for Applying Machine Learning
20. Partially Observable MDPs (POMDPs)