NBML Course

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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
+
*[[Machine_Learning/NBML/Machine Learning|Machine Learning]]
**The data
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**[[Machine_Learning/NBML/Machine Learning/Data|The data]]
**The model
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**[[Machine_Learning/NBML/Machine Learning/Model|The model]]
**Unsupervised vs. Supervised Learning
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**[[Machine_Learning/NBML/Machine Learning/Learning|Unsupervised vs. Supervised Learning]]
**Training a Model
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**[[Machine_Learning/NBML/Machine Learning/Training|Training a Model]]
***Maximum Likelihood
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***[[Machine_Learning/NBML/Machine Learning/Maximum Likelihood|Maximum Likelihood]]
***Optimization
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***[[Machine_Learning/NBML/Machine Learning/Optimization|Optimization]]
****Gradient Descent
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****[[Machine_Learning/NBML/Machine Learning/Optimization/Gradient Descent|Gradient Descent]]
****Lagrange Optimization
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****[[Machine_Learning/NBML/Machine Learning/Optimization/Lagrange Optimization|Lagrange Optimization]]
***Expectation-Maximization
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****[[Machine_Learning/NBML/Machine Learning/Optimization/Expectation-Maximization|Expectation Maxmimization]]
***Overfitting and Regularization
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***[[Machine_Learning/NBML/Machine Learning/Regularization|Overfitting and Regularization]]
***Bias-variance Tradeoff
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***[[Machine_Learning/NBML/Machine Learning/Bias-variance Tradeoff|Bias-Variance Tradeoff]]
  
 
==== Block 2: Linear Regression ====
 
==== Block 2: Linear Regression ====

Revision as of 00:37, 6 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 asking 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

Block 1: Basic Math and Machine Learning

Block 2: Linear Regression

  • Linear Regression
    • Least Squares Formulation
    • Maximum-likelihood Formulation
    • Regularization
      • Ridge Regression (L2)
      • Lasso Regression (L1)
      • Least-angle/Elastic Net Regression
    • Bayesian Linear Regression

Block 3: Linear Classification (non-SVM)

  • Linear Classification
    • Binary vs. Multi-class
      • One-versus-the-rest, one-versus-one
    • Discriminant Functions
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