NBML Course: Difference between revisions
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#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 === | ||
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=== 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]] | ||
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**[[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:
- 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!
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]
- K-Means Clustering
- Principle Component Analysis
- Independent Component Analysis
- Dimensional Reduction and Clustering for Visualization
- Clustering Techniques for Text Collections
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.