NBML Course: Difference between revisions
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**Vectors and Matricies | **Vectors and Matricies | ||
**Solving Linear Systems: Gaussian Elimination | **Solving Linear Systems: Gaussian Elimination | ||
** | **Vector Spaces | ||
**Eigenvectors and Eigenvalues | |||
**Quadratic Forms | |||
*Calculus | *Calculus | ||
**Derivatives, Gradients, and Hessians | |||
**Integration as Sums | |||
*Probability Theory | *Probability Theory | ||
**Distribution and Density Functions | |||
***Discrete Distributions | |||
***Continuous Distributions | |||
**Random Variables and Vectors | |||
**Expectation | |||
**Variance and Covariance | |||
**Correlation Functions | |||
**Law of Large Numbers | |||
**Information Theory | |||
***Entropy | |||
***Mutual Information | |||
*Machine Learning | *Machine Learning | ||
**The data | **The data | ||
Line 23: | Line 38: | ||
***Optimization | ***Optimization | ||
***Expectation-Maximization | ***Expectation-Maximization | ||
***Overfitting and Regularization | |||
***Bias-variance Tradeoff | |||
==== Block 2: Linear Regression and Classification ==== | ==== Block 2: Linear Regression and Classification ==== | ||
*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 | **Bayesian Linear Regression | ||
*Linear Classification |
Revision as of 23:23, 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
- Vector Spaces
- Eigenvectors and Eigenvalues
- Quadratic Forms
- Calculus
- Derivatives, Gradients, and Hessians
- Integration as Sums
- Probability Theory
- Distribution and Density Functions
- Discrete Distributions
- Continuous Distributions
- Random Variables and Vectors
- Expectation
- Variance and Covariance
- Correlation Functions
- Law of Large Numbers
- Information Theory
- Entropy
- Mutual Information
- Distribution and Density Functions
- Machine Learning
- The data
- The model
- Unsupervised vs. Supervised Learning
- Training a Model
- Maximum Likelihood
- Optimization
- Expectation-Maximization
- Overfitting and Regularization
- Bias-variance Tradeoff
Block 2: Linear Regression and Classification
- Linear Regression
- Least Squares Formulation
- Maximum-likelihood Formulation
- Regularization
- Ridge Regression (L2)
- Lasso Regression (L1)
- Least-angle/Elastic Net Regression
- Bayesian Linear Regression
- Linear Classification