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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, 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
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