NBML Course

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(Created page with '== Noisebridge Machine Learning Course == A bottom up hands-on curriculum for teaching Machine Learning at Noisebridge. === Online Machine Learning Courses === [http://www.stanf…')
 
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== Noisebridge Machine Learning Course ==
 
== Noisebridge Machine Learning Course ==
A bottom up hands-on curriculum for teaching Machine Learning at Noisebridge.
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We're trying to come up with a hands-on curriculum for teaching [[Machine_Learning|Machine Learning at Noisebridge]]. Please help out in any way you can!
  
 
=== Online Machine Learning Courses ===
 
=== Online Machine Learning Courses ===
[http://www.stanford.edu/class/cs229/ Stanford CS229]
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*[http://www.stanford.edu/class/cs229/ Stanford CS229]
[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT OCW 6.867]
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*[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT OCW 6.867]
  
===
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=== Curriculum ===
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==== Block 1: Basic Math and Machine Learning ====
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*Linear Algebra
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**Vectors and Matricies
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**Solving Linear Systems: Gaussian Elimination
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**
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*Calculus
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*Probability Theory
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*Machine Learning
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**The data
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**The model
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**Unsupervised vs. Supervised Learning
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**Training a Model
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***Maximum Likelihood
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***Optimization
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***Expectation-Maximization
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==== Block 2: Linear Regression and Classification ====
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*Linear Regression
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**Bayesian Linear Regression

Revision as of 23:13, 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!

Online Machine Learning Courses

Curriculum

Block 1: Basic Math and Machine Learning

  • Linear Algebra
    • Vectors and Matricies
    • Solving Linear Systems: Gaussian Elimination
  • Calculus
  • Probability Theory
  • Machine Learning
    • The data
    • The model
    • Unsupervised vs. Supervised Learning
    • Training a Model
      • Maximum Likelihood
      • Optimization
      • Expectation-Maximization

Block 2: Linear Regression and Classification

  • Linear Regression
    • Bayesian Linear Regression
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