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#Volunteer to teach a course in one of the subjects below | #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 | #Fill in one of the subjects below with links to learning material and related software | ||
#Show up to classes and | #Show up to classes and asking questions | ||
#Join the [https://www.noisebridge.net/mailman/listinfo/ml ML Mailing List] and talk about stuff | #Join the [https://www.noisebridge.net/mailman/listinfo/ml ML Mailing List] and talk about stuff | ||
#Don't talk shit on mathematics | #Don't talk shit on mathematics, it wants to be your friend. | ||
=== Online Machine Learning Courses === | === Online Machine Learning Courses === | ||
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=== Curriculum === | === Curriculum === | ||
==== The Fundamentals: Basic Math and Machine Learning Theory ==== | |||
==== The Fundamentals: Basic Math ==== | |||
*[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]] | *[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]] | ||
**[[Machine_Learning/NBML/Linear Algebra/Vectors and | **[[Machine_Learning/NBML/Linear Algebra/Vectors and Matricies|Vectors and Matricies]] | ||
**[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems|Solving Linear Systems | **[[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/Vector Spaces|Vector Spaces]] | ||
**[[Machine_Learning/NBML/Linear Algebra/Eigenvectors and Eigenvalues|Eigenvectors and Eigenvalues]] | **[[Machine_Learning/NBML/Linear Algebra/Eigenvectors and Eigenvalues|Eigenvectors and Eigenvalues]] | ||
**[[Machine_Learning/NBML/Linear Algebra/Quadratic Forms|Quadratic Forms]] | **[[Machine_Learning/NBML/Linear Algebra/Quadratic Forms|Quadratic Forms]] | ||
*[[Machine_Learning/NBML/Calculus|Calculus]] | *[[Machine_Learning/NBML/Calculus|Calculus]] | ||
**[[Machine_Learning/NBML/Calculus/Derivatives, Gradients, and Hessians|Derivatives, Gradients, and Hessians]] | **[[Machine_Learning/NBML/Calculus/Derivatives, Gradients, and Hessians|Derivatives, Gradients, and Hessians]] | ||
**[[Machine_Learning/NBML/Calculus/Integration|Integration]] | **[[Machine_Learning/NBML/Calculus/Integration|Integration]] | ||
*[[Machine_Learning/NBML/Probability|Probability Theory]] | *[[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|Distribution and Density Functions]] | ||
***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Discrete Distributions|Discrete Distributions]] | ***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Discrete Distributions|Discrete Distributions]] | ||
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**[[Machine_Learning/NBML/Probability/Information Theory|Information Theory]] | **[[Machine_Learning/NBML/Probability/Information Theory|Information Theory]] | ||
***[[Machine_Learning/NBML/Probability/Information Theory/Entropy|Entropy]] | ***[[Machine_Learning/NBML/Probability/Information Theory/Entropy|Entropy]] | ||
***[[Machine_Learning/NBML/Probability/Information Theory/Mutual Information|Mutual Information]] | ***[[Machine_Learning/NBML/Probability/Information Theory/Mutual Information|Mutual Information]] | ||
*[[Machine_Learning/NBML/ | *[[Machine_Learning/NBML/Machine Learning|Machine Learning]] | ||
*[[Machine_Learning/NBML/ | **[[Machine_Learning/NBML/Machine Learning/Data|The data]] | ||
**[[Machine_Learning/NBML/ | **[[Machine_Learning/NBML/Machine Learning/Model|The model]] | ||
**[[Machine_Learning/NBML/Machine Learning/Learning|Unsupervised vs. Supervised Learning]] | |||
**[[Machine_Learning/NBML/Machine Learning/Training|Training a Model]] | |||
***[[Machine_Learning/NBML/Machine Learning/Maximum Likelihood|Maximum Likelihood]] | |||
***[[Machine_Learning/NBML/Machine Learning/Optimization|Optimization]] | |||
****[[Machine_Learning/NBML/Machine Learning/Optimization/Gradient Descent|Gradient Descent]] | |||
****[[Machine_Learning/NBML/Machine Learning/Optimization/Lagrange Optimization|Lagrange Optimization]] | |||
****[[Machine_Learning/NBML/Machine Learning/Optimization/Expectation-Maximization|Expectation Maxmimization]] | |||
***[[Machine_Learning/NBML/Machine Learning/Regularization|Overfitting and Regularization]] | |||
***[[Machine_Learning/NBML/Machine Learning/Bias-variance Tradeoff|Bias-Variance Tradeoff]] | |||
==== Linear Regression ==== | |||
*[[Machine_Learning/NBML/Linear Regression|Linear Regression]] | |||
**[[Machine_Learning/NBML/Linear Regression/Least Squares|Least Squares Formulation]] | |||
**[[Machine_Learning/NBML/Linear Regression/Maximum Likelihood| Maximum Likelihood Formulation]] | |||
**[[Machine_Learning/NBML/Linear Regression/Regularization|Regularization]] | |||
***[[Machine_Learning/NBML/Linear Regression/Ridge|Ridge Regression (L2)]] | |||
***[[Machine_Learning/NBML/Linear Regression/Lasso|Lasso Regression (L1)]] | |||
***[[Machine_Learning/NBML/Linear Regression/LARS|Least-angle/Elastic Net Regression]] | |||
**[[Machine_Learning/NBML/Linear Regression/Bayesian|Bayesian Linear Regression]] | |||
==== Block 3: Linear Classification (non-SVM) ==== | |||
*Linear Classification | |||
**Binary vs. Multi-class | |||
***One-versus-the-rest, one-versus-one | |||
**Discriminant Functions |