# NBML Course

From Noisebridge

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

*Machine Learning | *Machine Learning | ||

**The data | **The data | ||

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***Bias-variance Tradeoff | ***Bias-variance Tradeoff | ||

− | ==== Block 2: Linear Regression | + | ==== Block 2: Linear Regression ==== |

*Linear Regression | *Linear Regression | ||

**Least Squares Formulation | **Least Squares Formulation | ||

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***Least-angle/Elastic Net Regression | ***Least-angle/Elastic Net Regression | ||

**Bayesian Linear Regression | **Bayesian Linear Regression | ||

+ | |||

+ | ==== Block 3: Linear Classification (non-SVM) ==== | ||

*Linear Classification | *Linear Classification | ||

+ | **Binary vs. Multi-class | ||

+ | ***One-versus-the-rest, one-versus-one | ||

+ | **Discriminant Functions |

## Revision as of 23:57, 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, 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 asking 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

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

- Linear Algebra
- Calculus
- Probability Theory
- Machine Learning
- The data
- The model
- Unsupervised vs. Supervised Learning
- Training a Model
- Maximum Likelihood
- Optimization
- Gradient Descent
- Lagrange Optimization

- Expectation-Maximization
- Overfitting and Regularization
- Bias-variance Tradeoff

#### Block 2: Linear Regression

- Linear Regression
- Least Squares Formulation
- Maximum-likelihood Formulation
- Regularization
- Ridge Regression (L2)
- Lasso Regression (L1)
- Least-angle/Elastic Net Regression

- Bayesian Linear Regression

#### Block 3: Linear Classification (non-SVM)

- Linear Classification
- Binary vs. Multi-class
- One-versus-the-rest, one-versus-one

- Discriminant Functions

- Binary vs. Multi-class