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Lecture 0
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Welcome to CMSACamp: Background and overview
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Lecture 1
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Exploring data: Into the tidyverse
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Lecture 2
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Data Visualization: The grammar of graphics and ggplot2
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Lecture 3
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Data Visualization: Visualizing 1D categorical and continuous variables, plus scatter plots
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Lecture 4
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Data Visualization: Visualizing 2D categorical and continuous by categorical, plus facets
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Lecture 5
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Data Visualization: Density estimation
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Lecture 6
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Clustering: K-means and hierarchical clustering
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Lecture 7
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Clustering: Gaussian mixture models
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Lecture 8
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Supervised Learning: Model assessment vs selection, and the bias-variance tradeoff
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Lecture 9
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Supervised Learning: Linear regression
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Lecture 10
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Supervised Learning: Generalized linear models
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Lecture 11
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Supervised Learning: Logistic regression
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Lecture 12
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Supervised Learning: Variable selection
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Lecture 13
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Supervised Learning: Regularization
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Lecture 14
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Unsupervised Learning: Principal components analysis
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Lecture 15
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Supervised Learning: Nonparametric regression
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Lecture 16
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Supervised Learning: Smoothing splines and GAMs
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Lecture 17
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Machine learning: Tree-based models
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Lecture 18
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Machine learning: Random forests and gradient-boosted trees
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