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Lecture 0
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Welcome to SURE: 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
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Lecture 4
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Data Visualization: Visualizing 2D categorical and continuous by categorical
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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
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Lecture 7
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Clustering: Hierarchical clustering
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Lecture 8
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Presentations: And working with xaringan and xaringanthemer
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Lecture 9
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Model-based clustering: Gaussian mixture models
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Lecture 10
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Supervised Learning: Model assessment vs selection
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Lecture 11
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Supervised Learning: Linear regression
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Lecture 12
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Supervised Learning: Intro to 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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Dimension Reduction: Principal components analysis (PCA)
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Lecture 15
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Supervised Learning: Principal component regression and partial least squares
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Lecture 16
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Supervised Learning: Generalized linear models (GLMs)
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Lecture 17
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Supervised Learning: Logistic regression
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Lecture 18
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Supervised Learning: From nonparametric regression to GAMs
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Lecture 19
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Machine learning: Decision trees
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Lecture 20
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Machine learning: Random forests and gradient-boosted trees
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Lecture 21
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Advanced topics: Multinomial logistic regression and multilevel models
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Lecture 22
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Advanced topics: More fun with classification
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