NBML Course

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(The Fundamentals: Basic Math)
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==== The Fundamentals: Basic Math ====
 
==== 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.''
+
''Note: it's not essential to understand everything in this section! But the more you learn, the more things will make sense. Wikipedia is your friend. ''
 
*[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]]
 
*[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]]
**[[Machine_Learning/NBML/Linear Algebra/Vectors and Matricies|Vectors and Matricies]]
+
**[[Machine_Learning/NBML/Linear Algebra/Vectors and Matrices|Vectors and Matrices]]
**[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems|Solving Linear Systems: Gaussian Elimination]]
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**[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems|Solving Linear Systems ]]
 +
***[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems/LU Decomposition |LU Decomposition]]
 
**[[Machine_Learning/NBML/Linear Algebra/Vector Spaces|Vector Spaces]]
 
**[[Machine_Learning/NBML/Linear Algebra/Vector Spaces|Vector Spaces]]
 +
**[[Machine_Learning/NBML/Linear Algebra/Vector Spaces/Orthogonalization algorithms|Orthogonalization algorithms]]
 
**[[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/Linear Algebra/Singular Value Decomposition (SVD) |Singular Value Decompostion (SVD)]]
 
*[[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]]
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**[[Machine_Learning/NBML/Calculus/Fourier Transform | Fourier Transform]]
 
*[[Machine_Learning/NBML/Probability|Probability Theory]]
 
*[[Machine_Learning/NBML/Probability|Probability Theory]]
 +
**[[Machine_Learning/NBML/Probability/Basic Probability|Basic Probability]]
 +
***[[Machine_Learning/NBML/Probability/Basic Probability/Bayes Theorem | Bayes Theorem]]
 
**[[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/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/Geometry for Computer Vision and Simulated Environments |Geometry for Computer Vision and Simulated Environments]]
 +
*[[Machine_Learning/NBML/Logic and Set Theory|Logic and Set Theory]]
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**[[Machine_Learning/NBML/Logic and Set Theory/Fuzzy Logic and Control Theory |Fuzzy Logic and Control Theory]]

Revision as of 19:45, 13 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. Wikipedia is your friend.

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