NBML Course

From Noisebridge
(Difference between revisions)
Jump to: navigation, search
m (The Fundamentals: Basic Math)
(The Fundamentals: Basic Math)
 
(8 intermediate revisions by 3 users not shown)
Line 12: Line 12:
  
 
=== 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]] ====
Line 66: Line 42:
  
 
==== [[Machine_Learning/NBML/GLM|Generalized Linear Models]] ====
 
==== [[Machine_Learning/NBML/GLM|Generalized Linear Models]] ====
 +
 +
==== [[Machine_Learning/NBML/GP|Gaussian Process]] ====
  
 
==== [[Machine_Learning/NBML/SVM|Support Vector Machines]] ====
 
==== [[Machine_Learning/NBML/SVM|Support Vector Machines]] ====
Line 79: Line 57:
 
*[[Machine_Learning/NBML/Clustering/PCA|Principle Component Analysis]]
 
*[[Machine_Learning/NBML/Clustering/PCA|Principle Component Analysis]]
 
*[[Machine_Learning/NBML/Clustering/ICA|Independent Component Analysis]]
 
*[[Machine_Learning/NBML/Clustering/ICA|Independent Component Analysis]]
 +
*[[Machine_Learning/NBML/Clustering/Dimensional Reduction for Visualization|Dimensional Reduction and Clustering for Visualization]]
 +
**[[Machine_Learning/NBML/Clustering/Dimensional Reduction for Visualization/Self Organizing Map (algebraic perspective) | Self Organizing Map (algebraic perspective)]]
 +
**[[Machine_Learning/NBML/Clustering/Dimensional Reduction for Visualization/Supervised Methods and Refinement (LVQ)|Supervised Methods and Refinement (LVQ)]]
 +
*[[Machine_Learning/NBML/Clustering/Clustering Techniques for Text |Clustering Techniques for Text Collections]]
 +
**[[Machine_Learning/NBML/Clustering/Clustering Techniques for Text/Spherical K-Means |Spherical K-Means]]
 +
**[[Machine_Learning/NBML/Clustering/Clustering Techniques for Text/Word Sense Disambiguation (Sense Clusters)|Word Sense Disambiguation (Sense Clusters)]]
 +
**[[Machine_Learning/NBML/Clustering/Clustering Techniques for Text/Latent Semantic Indexing (LSI)|Latent Semantic Indexing (LSI)]]
 +
***[[Machine_Learning/NBML/Clustering/Clustering Techniques for Text/Latent Semantic Indexing (LSI)/Keyword Relatedness Clustering (Semantic Engine)|Keyword Relatedness Clustering (Semantic Engine)]]
 +
**[[Machine_Learning/NBML/Clustering/Clustering Techniques for Text/Text Clustering with Self Organizing Map (WebSOM)|Text Clustering with Self Organizing Map (WebSOM)]]
  
 
==== [[Machine_Learning/NBML/Graphical Models|Graphical Models]] ====
 
==== [[Machine_Learning/NBML/Graphical Models|Graphical Models]] ====
Line 85: Line 72:
  
 
==== [[Machine_Learning/NBML/HMM|Hidden Markov Models]] ====
 
==== [[Machine_Learning/NBML/HMM|Hidden Markov Models]] ====
 +
 +
==== [[ Other Perspectives | Other Perspectives]] ====
 +
*[[Machine_Learning/Linguistics and The Role of Language | Linguistics and The Role of Language]]
 +
**[[Machine_Learning/Symbolic Methods and Machine Understanding |Symbolic Methods and Machine Understanding]]
 +
*[[Machine_Learning/Simulation and Integrated Software Systems |Simulation and Integrated Software Systems]]
 +
**[[Machine_Learning/Autonomous Agents and Evolutionary (Learning) Algorithms |Autonomous Agents and Evolutionary (Learning) Algorithms]]
 +
 +
==== 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. ''
 +
*[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]]
 +
**[[Machine_Learning/NBML/Linear Algebra/Vectors and Matrices|Vectors and Matrices]]
 +
**[[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/Orthogonalization algorithms|Orthogonalization algorithms]]
 +
**[[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/Singular Value Decomposition (SVD) |Singular Value Decompostion (SVD)]]
 +
*[[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/Calculus/Fourier Transform | Fourier Transform]]
 +
**[[Machine_Learning/NBML/Calculus/Vector Calculus | Vector Calculus]]
 +
***[[Machine_Learning/NBML/Calculus/Vector Calculus/Optimization, Duality, Lagrange Multipliers and Kuhn-Tucker Theorem |Optimization, Duality, Lagrange Multipliers and Kuhn-Tucker Theorem ]]
 +
*[[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/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/Relative Entropy|Relative Entropy]]
 +
***[[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]]
 +
**[[Machine_Learning/NBML/Logic and Set Theory/Fuzzy Logic and Control Theory |Fuzzy Logic and Control Theory]]

Latest revision as of 19:47, 16 April 2011

Contents

[edit] 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!

[edit] Online Machine Learning Courses

[edit] Curriculum

[edit] Machine Learning

[edit] Linear Regression

[edit] Linear Classification

[edit] Generalized Linear Models

[edit] Gaussian Process

[edit] Support Vector Machines

[edit] Neural Networks

[edit] Clustering and Dimensional Reduction

[edit] Graphical Models

[edit] Hidden Markov Models

[edit] Other Perspectives

[edit] 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.

Personal tools