NBML Course

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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!
+
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, such as:
 +
#Volunteering to teach a course in one of the subjects below
 +
#Filling in one of the subjects below with links to learning material and related software
 +
#Showing up to classes and asking questions
 +
#Don't talk shit on mathematics, it wants to be your friend.
  
 
=== Online Machine Learning Courses ===
 
=== Online Machine Learning Courses ===

Revision as of 23:35, 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, such as:

  1. Volunteering to teach a course in one of the subjects below
  2. Filling in one of the subjects below with links to learning material and related software
  3. Showing up to classes and asking questions
  4. 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
    • 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
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