# NBML Course

From Noisebridge

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== Noisebridge Machine Learning Course == | == Noisebridge Machine Learning Course == | ||

− | + | We're trying to come up with a hands-on curriculum for teaching [[Machine_Learning|Machine Learning at Noisebridge]]. Please help out in any way you can! | |

=== Online Machine Learning Courses === | === Online Machine Learning Courses === | ||

− | [http://www.stanford.edu/class/cs229/ Stanford CS229] | + | *[http://www.stanford.edu/class/cs229/ Stanford CS229] |

− | [http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT OCW 6.867] | + | *[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT OCW 6.867] |

− | === | + | === Curriculum === |

+ | |||

+ | ==== Block 1: Basic Math and Machine Learning ==== | ||

+ | *Linear Algebra | ||

+ | **Vectors and Matricies | ||

+ | **Solving Linear Systems: Gaussian Elimination | ||

+ | ** | ||

+ | *Calculus | ||

+ | *Probability Theory | ||

+ | *Machine Learning | ||

+ | **The data | ||

+ | **The model | ||

+ | **Unsupervised vs. Supervised Learning | ||

+ | **Training a Model | ||

+ | ***Maximum Likelihood | ||

+ | ***Optimization | ||

+ | ***Expectation-Maximization | ||

+ | |||

+ | ==== Block 2: Linear Regression and Classification ==== | ||

+ | *Linear Regression | ||

+ | **Bayesian Linear Regression |

## Revision as of 23:13, 5 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!

### Online Machine Learning Courses

### Curriculum

#### Block 1: Basic Math and Machine Learning

- Linear Algebra
- Vectors and Matricies
- Solving Linear Systems: Gaussian Elimination

- Calculus
- Probability Theory
- Machine Learning
- The data
- The model
- Unsupervised vs. Supervised Learning
- Training a Model
- Maximum Likelihood
- Optimization
- Expectation-Maximization

#### Block 2: Linear Regression and Classification

- Linear Regression
- Bayesian Linear Regression