==== Winter 2018 - CS401r - Modern Data Analysis with Statistical ML ====

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=== Resources ===

[[cs401r_w2016:lab_notes|General notes on ipython and seaborn]]

You should become intimately familiar with [[http://docs.scipy.org/doc/numpy-1.10.1/user/basics.broadcasting.html|numpy's broadcasting]]

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=== Labs ===

[[cs401r_w2016:lab1|Lab 1 - Anaconda and pandas]]

[[cs401r_w2016:lab2|Lab 2 - Bayesian concept learning]]

[[cs401r_w2016:lab5|Lab 3 - MNIST with KDE]]

[[cs401r_w2016:lab4|Lab 4 - Gaussian process regression]]

[[cs401r_w2016:lab14|Lab 5 - Large-scale Gaussian process regression]]

[[cs401r_w2016:lab13|Lab 6 - Expectation maximization]]

[[cs401r_w2016:lab7|Lab 7 - Kalman filter]]

[[cs401r_w2016:lab8|Lab 8 - Localization with particle filters]]

[[cs401r_w2016:lab9|Lab 9 - LDA, General Conference, and Gibbs sampling]]

[[cs401r_w2016:lab10|Lab 10 - Metropolis Hastings and Hamiltonian MCMC]]

[[cs401r_w2016:lab12|Lab 11 - Recommender system]]

[[cs401r_w2016:fp|Final project]]

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Old labs we probably won't use

[[cs401r_w2016:lab3|Lab 3 - basic PDF library]]

[[cs401r_w2016:lab11|Lab 11 - Bayesian super resolution]]