Winter 2017 - CS401r - Modern Data Analysis with Statistical ML


Resources

General notes on ipython and seaborn

You should become intimately familiar with numpy's broadcasting


Labs

Lab 1 - Anaconda and pandas

Lab 2 - Bayesian concept learning

Lab 3 - MNIST with KDE

Lab 4 - Gaussian process regression

Lab 5 - Large-scale Gaussian process regression

Lab 6 - Expectation maximization

Lab 7 - Kalman filter

Lab 8 - Localization with particle filters

Lab 9 - LDA, General Conference, and Gibbs sampling

Lab 10 - Metropolis Hastings and Hamiltonian MCMC

Lab 11 - Recommender system

Final project


Old labs we probably won't use

Lab 3 - basic PDF library

Lab 11 - Bayesian super resolution