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General notes on ipython and seaborn
Lab 1 - install anaconda
Lab 2 - Bayesian concept learning
Lab 3 - basic PDF library
Lab 4 - Gaussian process regression
Lab 5 - MNIST with KDE
Lab 6 - ???
Lab 7 - Kalman filter
Lab 8 - localization with particle filters
Lab 9 - LDA, wikipedia, and Gibbs sampling
Lab 10 - improved generative images with MCMC
Lab 11 - Bayesian super resolution
Lab 12 - weather data with BP
Lab 13 - recommender system