NBML Course: Difference between revisions
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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 | **[[Machine_Learning/NBML/Linear Algebra/Vectors and Matricies|Vectors and Matricies]] | ||
**Solving Linear Systems: Gaussian Elimination | **[[Machine_Learning/NBML/Linear Algebra/Solving Linear Systems|Solving Linear Systems: Gaussian Elimination]] | ||
**Vector Spaces | **[[Machine_Learning/NBML/Linear Algebra/Vector Spaces|Vector Spaces]] | ||
**Eigenvectors and Eigenvalues | **[[Machine_Learning/NBML/Linear Algebra/Eigenvectors and Eigenvalues|Eigenvectors and Eigenvalues]] | ||
**Quadratic Forms | **[[Machine_Learning/NBML/Linear Algebra/Quadratic Forms|Quadratic Forms]] | ||
*Calculus | *[[Machine_Learning/NBML/Calculus|Calculus]] | ||
**Derivatives, Gradients, and Hessians | **[[Machine_Learning/NBML/Calculus/Derivatives, Gradients, and Hessians|Derivatives, Gradients, and Hessians]] | ||
**Integration | **[[Machine_Learning/NBML/Calculus/Integration|Integration]] | ||
*Probability Theory | *[[Machine_Learning/NBML/Probability|Probability Theory]] | ||
**Distribution and Density Functions | **[[Machine_Learning/NBML/Probability/Distribution and Density Functions|Distribution and Density Functions]] | ||
***Discrete Distributions | ***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Discrete Distributions|Discrete Distributions]] | ||
***Continuous Distributions | ***[[Machine_Learning/NBML/Probability/Distribution and Density Functions/Continuous Distributions|Continuous Distributions]] | ||
**Random Variables and Vectors | **[[Machine_Learning/NBML/Probability/Random Variables and Vectors|Random Variables and Vectors]] | ||
**Expectation | **[[Machine_Learning/NBML/Probability/Expectation|Expectation]] | ||
**Variance and Covariance | **[[Machine_Learning/NBML/Probability/Variance and Covariance|Variance and Covariance]] | ||
**Correlation Functions | **[[Machine_Learning/NBML/Probability/Correlation Functions|Correlation Functions]] | ||
**Law of Large Numbers | **[[Machine_Learning/NBML/Probability/Law of Large Numbers|Law of Large Numbers]] | ||
**Information Theory | **[[Machine_Learning/NBML/Probability/Information Theory|Information Theory]] | ||
***Entropy | ***[[Machine_Learning/NBML/Probability/Information Theory/Entropy|Entropy]] | ||
***Mutual Information | ***[[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 | ==== 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
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
- Linear Algebra
- Calculus
- Probability Theory
- Machine Learning
- The data
- The model
- Unsupervised vs. Supervised Learning
- Training a Model
- Maximum Likelihood
- Optimization
- Gradient Descent
- Lagrange Optimization
- Expectation-Maximization
- Overfitting and Regularization
- Bias-variance Tradeoff
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