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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! | ||
=== Online Machine Learning Courses === | === Online Machine Learning Courses === | ||
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=== Curriculum === | === 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 | |||
* | |||