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=== Curriculum === | === Curriculum === | ||
==== [[Machine_Learning/NBML/Machine Learning|Machine Learning]] ==== | ==== [[Machine_Learning/NBML/Machine Learning|Machine Learning]] ==== | ||
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==== [[Machine_Learning/NBML/HMM|Hidden Markov Models]] ==== | ==== [[Machine_Learning/NBML/HMM|Hidden Markov Models]] ==== | ||
==== 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.'' | |||
*[[Machine_Learning/NBML/Linear Algebra|Linear Algebra]] | |||
**[[Machine_Learning/NBML/Linear Algebra/Vectors and Matricies|Vectors and Matricies]] | |||
**[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems|Solving Linear Systems: Gaussian Elimination]] | |||
**[[Machine_Learning/NBML/Linear Algebra/Vector Spaces|Vector Spaces]] | |||
**[[Machine_Learning/NBML/Linear Algebra/Eigenvectors and Eigenvalues|Eigenvectors and Eigenvalues]] | |||
**[[Machine_Learning/NBML/Linear Algebra/Quadratic Forms|Quadratic Forms]] | |||
*[[Machine_Learning/NBML/Calculus|Calculus]] | |||
**[[Machine_Learning/NBML/Calculus/Derivatives, Gradients, and Hessians|Derivatives, Gradients, and Hessians]] | |||
**[[Machine_Learning/NBML/Calculus/Integration|Integration]] | |||
*[[Machine_Learning/NBML/Probability|Probability Theory]] | |||
**[[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/Continuous Distributions|Continuous Distributions]] | |||
**[[Machine_Learning/NBML/Probability/Random Variables and Vectors|Random Variables and Vectors]] | |||
**[[Machine_Learning/NBML/Probability/Expectation|Expectation]] | |||
**[[Machine_Learning/NBML/Probability/Variance and Covariance|Variance and Covariance]] | |||
**[[Machine_Learning/NBML/Probability/Correlation Functions|Correlation Functions]] | |||
**[[Machine_Learning/NBML/Probability/Law of Large Numbers|Law of Large Numbers]] | |||
**[[Machine_Learning/NBML/Probability/Information Theory|Information Theory]] | |||
***[[Machine_Learning/NBML/Probability/Information Theory/Entropy|Entropy]] | |||
***[[Machine_Learning/NBML/Probability/Information Theory/Mutual Information|Mutual Information]] |
Revision as of 21:00, 6 January 2011
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!
Online Machine Learning Courses
Curriculum
Machine Learning
Linear Regression
Linear Classification
Generalized Linear Models
Support Vector Machines
Neural Networks
Clustering and Dimensional Reduction
Graphical Models
Hidden Markov Models
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.