NBML Course

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==== Block 1: Basic Math and Machine Learning ====
 
==== Block 1: Basic Math and Machine Learning ====
*Linear Algebra
+
*[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]]
**Vectors and Matricies
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**[[Machine_Learning/NBML/Linear Algebra/Vectors and Matricies|Vectors and Matricies]]
**Solving Linear Systems: Gaussian Elimination
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**[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems|Solving Linear Systems: Gaussian Elimination]]
**Vector Spaces
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**[[Machine_Learning/NBML/Linear Algebra/Vector Spaces|Vector Spaces]]
**Eigenvectors and Eigenvalues
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**[[Machine_Learning/NBML/Linear Algebra/Eigenvectors and Eigenvalues|Eigenvectors and Eigenvalues]]
**Quadratic Forms
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**[[Machine_Learning/NBML/Linear Algebra/Quadratic Forms|Quadratic Forms]]
*Calculus
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*[[Machine_Learning/NBML/Calculus|Calculus]]
**Derivatives, Gradients, and Hessians
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**[[Machine_Learning/NBML/Calculus/Derivatives, Gradients, and Hessians|Derivatives, Gradients, and Hessians]]
**Integration as Sums
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**[[Machine_Learning/NBML/Calculus/Integration|Integration]]
*Probability Theory
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*[[Machine_Learning/NBML/Probability|Probability Theory]]
**Distribution and Density Functions
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**[[Machine_Learning/NBML/Probability/Distribution and Density Functions|Distribution and Density Functions]]
***Discrete Distributions
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***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Discrete Distributions|Discrete Distributions]]
***Continuous Distributions
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***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Continuous Distributions|Continuous Distributions]]
**Random Variables and Vectors
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**[[Machine_Learning/NBML/Probability/Random Variables and Vectors|Random Variables and Vectors]]
**Expectation
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**[[Machine_Learning/NBML/Probability/Expectation|Expectation]]
**Variance and Covariance
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**[[Machine_Learning/NBML/Probability/Variance and Covariance|Variance and Covariance]]
**Correlation Functions
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**[[Machine_Learning/NBML/Probability/Correlation Functions|Correlation Functions]]
**Law of Large Numbers
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**[[Machine_Learning/NBML/Probability/Law of Large Numbers|Law of Large Numbers]]
**Information Theory
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**[[Machine_Learning/NBML/Probability/Information Theory|Information Theory]]
***Entropy  
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***[[Machine_Learning/NBML/Probability/Information Theory/Entropy|Entropy]]
***Mutual Information
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***[[Machine_Learning/NBML/Probability/Information Theory/Mutual Information|Mutual Information]]
 
*Machine Learning
 
*Machine Learning
 
**The data
 
**The data
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***Bias-variance Tradeoff
 
***Bias-variance Tradeoff
  
==== Block 2: Linear Regression and Classification ====
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==== Block 2: Linear Regression ====
 
*Linear Regression
 
*Linear Regression
 
**Least Squares Formulation
 
**Least Squares Formulation
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***Least-angle/Elastic Net Regression
 
***Least-angle/Elastic Net Regression
 
**Bayesian Linear Regression
 
**Bayesian Linear Regression
 +
 +
==== Block 3: Linear Classification (non-SVM) ====
 
*Linear Classification
 
*Linear Classification
 +
**Binary vs. Multi-class
 +
***One-versus-the-rest, one-versus-one
 +
**Discriminant Functions

Revision as of 23:57, 5 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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