# Difference between revisions of "NBML Course"

(→Noisebridge Machine Learning Course) |
(→The Fundamentals: Basic Math) |
||

Line 109: | Line 109: | ||

**[[Machine_Learning/NBML/Probability/Information Theory|Information Theory]] | **[[Machine_Learning/NBML/Probability/Information Theory|Information Theory]] | ||

***[[Machine_Learning/NBML/Probability/Information Theory/Entropy|Entropy]] | ***[[Machine_Learning/NBML/Probability/Information Theory/Entropy|Entropy]] | ||

+ | ***[[Machine_Learning/NBML/Probability/Information Theory/Relative Entropy|Relative Entropy]] | ||

***[[Machine_Learning/NBML/Probability/Information Theory/Mutual Information|Mutual Information]] | ***[[Machine_Learning/NBML/Probability/Information Theory/Mutual Information|Mutual Information]] | ||

*[[Machine_Learning/NBML/Geometry for Computer Vision and Simulated Environments |Geometry for Computer Vision and Simulated Environments]] | *[[Machine_Learning/NBML/Geometry for Computer Vision and Simulated Environments |Geometry for Computer Vision and Simulated Environments]] | ||

*[[Machine_Learning/NBML/Logic and Set Theory|Logic and Set Theory]] | *[[Machine_Learning/NBML/Logic and Set Theory|Logic and Set Theory]] | ||

**[[Machine_Learning/NBML/Logic and Set Theory/Fuzzy Logic and Control Theory |Fuzzy Logic and Control Theory]] | **[[Machine_Learning/NBML/Logic and Set Theory/Fuzzy Logic and Control Theory |Fuzzy Logic and Control Theory]] |

## Latest revision as of 02:47, 17 April 2011

## Contents

- 1 Noisebridge Machine Learning Course
- 1.1 Online Machine Learning Courses
- 1.2 Curriculum
- 1.2.1 Machine Learning
- 1.2.2 Linear Regression
- 1.2.3 Linear Classification
- 1.2.4 Generalized Linear Models
- 1.2.5 Gaussian Process
- 1.2.6 Support Vector Machines
- 1.2.7 Neural Networks
- 1.2.8 Clustering and Dimensional Reduction
- 1.2.9 Graphical Models
- 1.2.10 Hidden Markov Models
- 1.2.11 Other Perspectives
- 1.2.12 The Fundamentals: Basic Math

## Noisebridge Machine Learning Course[edit]

We're trying to come up with a hands-on curriculum for teaching Machine Learning at Noisebridge. Please help out in any way you can, such as:

- Volunteer to teach a course in one of the subjects below
- Fill in one of the subjects below with links to learning material and related software
- Show up to classes and ask questions
- Join the ML Mailing List and talk about stuff
- Don't talk shit on mathematics - it wants to be your friend!

### Online Machine Learning Courses[edit]

### Curriculum[edit]

#### Machine Learning[edit]

#### Linear Regression[edit]

#### Linear Classification[edit]

#### Generalized Linear Models[edit]

#### Gaussian Process[edit]

#### Support Vector Machines[edit]

#### Neural Networks[edit]

#### Clustering and Dimensional Reduction[edit]

- K-Means Clustering
- Principle Component Analysis
- Independent Component Analysis
- Dimensional Reduction and Clustering for Visualization
- Clustering Techniques for Text Collections

#### Graphical Models[edit]

#### Hidden Markov Models[edit]

#### Other Perspectives[edit]

#### The Fundamentals: Basic Math[edit]

*Note: it's not essential to understand everything in this section! But the more you learn, the more things will make sense. Wikipedia is your friend. *