NBML Course: Difference between revisions

From Noisebridge
Jump to navigation Jump to search
mNo edit summary
 
(7 intermediate revisions by 2 users not shown)
Line 42: 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 55: 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 61: 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 ====
==== 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]]
**[[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]]
**[[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|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]]
Line 84: Line 109:
**[[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/Relative Entropy|Relative 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]]
**[[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

Noisebridge Machine Learning Course[edit]

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[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[edit]

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.