# NBML Course

From Noisebridge

(Difference between revisions)

Mschachter (Talk | contribs) |
(→The Fundamentals: Basic Math) |
||

(23 intermediate revisions by 3 users not shown) | |||

Line 3: | Line 3: | ||

#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 ask 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 === | ||

Line 13: | Line 13: | ||

=== Curriculum === | === Curriculum === | ||

− | ==== | + | ==== [[Machine_Learning/NBML/Machine Learning|Machine Learning]] ==== |

+ | *[[Machine_Learning/NBML/Machine Learning/Data|The data]] | ||

+ | *[[Machine_Learning/NBML/Machine Learning/Model|The model]] | ||

+ | **[[Machine_Learning/NBML/Machine Learning/Model/Discriminative vs Generative|Discriminative vs Generative Models]] | ||

+ | *[[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]] | ||

+ | |||

+ | ==== [[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]] | ||

+ | |||

+ | ==== [[Machine_Learning/NBML/Linear Classification|Linear Classification]] ==== | ||

+ | *[[Machine_Learning/NBML/Linear Classification/Fishers Discriminant|Fisher's Linear Discriminant]] | ||

+ | *[[Machine_Learning/NBML/Linear Classification/Logistic|Logistic Regression]] | ||

+ | *[[Machine_Learning/NBML/Linear Classification/Probit|Probit Regression]] | ||

+ | |||

+ | ==== [[Machine_Learning/NBML/GLM|Generalized Linear Models]] ==== | ||

+ | |||

+ | ==== [[Machine_Learning/NBML/GP|Gaussian Process]] ==== | ||

+ | |||

+ | ==== [[Machine_Learning/NBML/SVM|Support Vector Machines]] ==== | ||

+ | |||

+ | ==== [[Machine_Learning/NBML/Neural Networks|Neural Networks]] ==== | ||

+ | *[[Machine_Learning/NBML/Neural Networks/Feedforward|Feedforward Nets]] | ||

+ | *[[Machine_Learning/NBML/Neural Networks/Hopfield|Hopfield Nets/Autoassociators]] | ||

+ | *[[Machine_Learning/NBML/Neural Networks/Recurrent|Recurrent Nets/Boltzmann Machines]] | ||

+ | *[[Machine_Learning/NBML/Neural Networks/Deep Belief|Deep Belief Nets]] | ||

+ | |||

+ | ==== [[Machine_Learning/NBML/Clustering|Clustering and Dimensional Reduction]] ==== | ||

+ | *[[Machine_Learning/NBML/Clustering/KMeans|K-Means Clustering]] | ||

+ | *[[Machine_Learning/NBML/Clustering/PCA|Principle 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/Bayesian|Bayesian Networks]] | ||

+ | *[[Machine_Learning/NBML/Graphical Models/Markov Random Fields|Markov Random Fields]] | ||

+ | |||

+ | ==== [[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|Linear Algebra]] | ||

− | **[[Machine_Learning/NBML/Linear Algebra/Vectors and | + | **[[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|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 34: | 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/ | + | *[[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/ | + | **[[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

## [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:

- 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
- Show up to classes and ask questions
- Join the ML Mailing List and talk about stuff
- 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

- K-Means Clustering
- Principle Component Analysis
- Independent Component Analysis
- Dimensional Reduction and Clustering for Visualization
- Clustering Techniques for Text Collections

#### [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. *