# NBML Course

From Noisebridge

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**Vectors and Matricies | **Vectors and Matricies | ||

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

− | ** | + | **Vector Spaces |

+ | **Eigenvectors and Eigenvalues | ||

+ | **Quadratic Forms | ||

*Calculus | *Calculus | ||

+ | **Derivatives, Gradients, and Hessians | ||

+ | **Integration as Sums | ||

*Probability Theory | *Probability Theory | ||

+ | **Distribution and Density Functions | ||

+ | ***Discrete Distributions | ||

+ | ***Continuous Distributions | ||

+ | **Random Variables and Vectors | ||

+ | **Expectation | ||

+ | **Variance and Covariance | ||

+ | **Correlation Functions | ||

+ | **Law of Large Numbers | ||

+ | **Information Theory | ||

+ | ***Entropy | ||

+ | ***Mutual Information | ||

*Machine Learning | *Machine Learning | ||

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

Line 23: | Line 38: | ||

***Optimization | ***Optimization | ||

***Expectation-Maximization | ***Expectation-Maximization | ||

+ | ***Overfitting and Regularization | ||

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

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

*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 | **Bayesian Linear Regression | ||

+ | *Linear Classification |

## Revision as of 23:23, 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
- Vector Spaces
- Eigenvectors and Eigenvalues
- Quadratic Forms

- Calculus
- Derivatives, Gradients, and Hessians
- Integration as Sums

- Probability Theory
- Distribution and Density Functions
- Discrete Distributions
- Continuous Distributions

- Random Variables and Vectors
- Expectation
- Variance and Covariance
- Correlation Functions
- Law of Large Numbers
- Information Theory
- Entropy
- Mutual Information

- Distribution and Density Functions
- Machine Learning
- The data
- The model
- Unsupervised vs. Supervised Learning
- Training a Model
- Maximum Likelihood
- Optimization
- Expectation-Maximization
- Overfitting and Regularization
- Bias-variance Tradeoff

#### Block 2: Linear Regression and Classification

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

- Bayesian Linear Regression

- Linear Classification