NBML Course: Difference between revisions
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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 | **[[Machine_Learning/NBML/Machine Learning/Data|The data]] | ||
**The model | **[[Machine_Learning/NBML/Machine Learning/Model|The model]] | ||
**Unsupervised vs. Supervised Learning | **[[Machine_Learning/NBML/Machine Learning/Learning|Unsupervised vs. Supervised Learning]] | ||
**Training a Model | **[[Machine_Learning/NBML/Machine Learning/Training|Training a Model]] | ||
***Maximum Likelihood | ***[[Machine_Learning/NBML/Machine Learning/Maximum Likelihood|Maximum Likelihood]] | ||
***Optimization | ***[[Machine_Learning/NBML/Machine Learning/Optimization|Optimization]] | ||
****Gradient Descent | ****[[Machine_Learning/NBML/Machine Learning/Optimization/Gradient Descent|Gradient Descent]] | ||
****Lagrange Optimization | ****[[Machine_Learning/NBML/Machine Learning/Optimization/Lagrange Optimization|Lagrange Optimization]] | ||
***Expectation-Maximization | ****[[Machine_Learning/NBML/Machine Learning/Optimization/Expectation-Maximization|Expectation Maxmimization]] | ||
***Overfitting and Regularization | ***[[Machine_Learning/NBML/Machine Learning/Regularization|Overfitting and Regularization]] | ||
***Bias-variance Tradeoff | ***[[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
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 asking 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
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
- Binary vs. Multi-class