# NBML Course

From Noisebridge

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**[[Machine_Learning/NBML/Calculus/Integration|Integration]] | **[[Machine_Learning/NBML/Calculus/Integration|Integration]] | ||

**[[Machine_Learning/NBML/Calculus/Fourier Transform | Fourier Transform]] | **[[Machine_Learning/NBML/Calculus/Fourier Transform | Fourier Transform]] | ||

+ | **[[Machine_Learning/NBML/Calculus/Vector Calculus | Vector Calculus]] | ||

+ | ***[[Machine_Learning/NBML/Calculus/Vector Calculus/Optimization, Duality, Lagrange Multipliers and Kuhn-Tucker Theorem |Optimization, Duality, Lagrange Multipliers and Kuhn-Tucker Theorem ]] | ||

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

**[[Machine_Learning/NBML/Probability/Basic Probability|Basic Probability]] | **[[Machine_Learning/NBML/Probability/Basic Probability|Basic Probability]] |

## Revision as of 19:56, 13 January 2011

## Contents |

## Noisebridge Machine Learning Course

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

### Curriculum

#### Machine Learning

#### Linear Regression

#### Linear Classification

#### Generalized Linear Models

#### Support Vector Machines

#### Neural Networks

#### Clustering and Dimensional Reduction

#### Graphical Models

#### Hidden Markov Models

#### The Fundamentals: Basic Math

*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. *