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== Noisebridge Machine Learning Course == | == Noisebridge Machine Learning Course == | ||
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] | *[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] | *[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT OCW 6.867] | ||
=== | === 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 |
Revision as of 23:13, 5 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!
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