NBML Course

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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
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