NBML Course

From Noisebridge
(Difference between revisions)
Jump to: navigation, search
Line 12: Line 12:
 
**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

Contents

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
  • 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
Personal tools