# NBML Course

From Noisebridge

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=== Curriculum === | === Curriculum === | ||

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==== [[Machine_Learning/NBML/Machine Learning|Machine Learning]] ==== | ==== [[Machine_Learning/NBML/Machine Learning|Machine Learning]] ==== | ||

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==== [[Machine_Learning/NBML/HMM|Hidden Markov Models]] ==== | ==== [[Machine_Learning/NBML/HMM|Hidden Markov Models]] ==== | ||

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+ | ==== 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.'' | ||

+ | *[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]] | ||

+ | **[[Machine_Learning/NBML/Linear Algebra/Vectors and Matricies|Vectors and Matricies]] | ||

+ | **[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems|Solving Linear Systems: Gaussian Elimination]] | ||

+ | **[[Machine_Learning/NBML/Linear Algebra/Vector Spaces|Vector Spaces]] | ||

+ | **[[Machine_Learning/NBML/Linear Algebra/Eigenvectors and Eigenvalues|Eigenvectors and Eigenvalues]] | ||

+ | **[[Machine_Learning/NBML/Linear Algebra/Quadratic Forms|Quadratic Forms]] | ||

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

+ | **[[Machine_Learning/NBML/Calculus/Derivatives, Gradients, and Hessians|Derivatives, Gradients, and Hessians]] | ||

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

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

+ | **[[Machine_Learning/NBML/Probability/Distribution and Density Functions|Distribution and Density Functions]] | ||

+ | ***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Discrete Distributions|Discrete Distributions]] | ||

+ | ***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Continuous Distributions|Continuous Distributions]] | ||

+ | **[[Machine_Learning/NBML/Probability/Random Variables and Vectors|Random Variables and Vectors]] | ||

+ | **[[Machine_Learning/NBML/Probability/Expectation|Expectation]] | ||

+ | **[[Machine_Learning/NBML/Probability/Variance and Covariance|Variance and Covariance]] | ||

+ | **[[Machine_Learning/NBML/Probability/Correlation Functions|Correlation Functions]] | ||

+ | **[[Machine_Learning/NBML/Probability/Law of Large Numbers|Law of Large Numbers]] | ||

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

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

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

## Revision as of 21:00, 6 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.*