NBML Course

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=== Curriculum ===
 
=== Curriculum ===
 
==== 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]]
 
  
 
==== [[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]] ====
 +
 +
==== 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:

  1. Volunteer to teach a course in one of the subjects below
  2. Fill in one of the subjects below with links to learning material and related software
  3. Show up to classes and ask questions
  4. Join the ML Mailing List and talk about stuff
  5. 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.

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