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- | ==== Winter 2016 - CS401r - Modern Data Analysis with Statistical ML ==== | ||
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- | === Labs === | ||
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- | [[cs401r_w2016:lab_notes|General notes on ipython and seaborn]] | ||
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- | [[cs401r_w2016:lab1|Lab 1 - Anaconda and pandas]] | ||
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- | [[cs401r_w2016:lab2|Lab 2 - Bayesian concept learning]] | ||
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- | [[cs401r_w2016:lab5|Lab 3 - MNIST with KDE]] | ||
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- | [[cs401r_w2016:lab4|Lab 4 - Gaussian process regression]] | ||
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- | [[cs401r_w2016:lab14|Lab 5 - Large-scale Gaussian process regression]] | ||
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- | [[cs401r_w2016:lab13|Lab 6 - Expectation maximization]] | ||
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- | [[cs401r_w2016:lab7|Lab 7 - Kalman filter]] | ||
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- | [[cs401r_w2016:lab8|Lab 8 - Localization with particle filters]] | ||
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- | [[cs401r_w2016:lab9|Lab 9 - LDA, General Conference, and Gibbs sampling]] | ||
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- | [[cs401r_w2016:lab10|Lab 10 - Metropolis Hastings and Hamiltonian MCMC]] | ||
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- | [[cs401r_w2016:lab12|Lab 11 - Recommender system]] | ||
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- | [[cs401r_w2016:fp|Final project]] | ||
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- | ---------------- | ||
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- | Old labs we probably won't use | ||
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- | [[cs401r_w2016:lab3|Lab 3 - basic PDF library]] | ||
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- | [[cs401r_w2016:lab11|Lab 11 - Bayesian super resolution]] | ||
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