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Preface
Preliminaries
Introduction to Python
Essential Tools: Git and Jupyter
Essential Tools: Pandas
Linear Algebra Refresher
Probability and Statistics Refresher
Clustering
Distances and Timeseries
\(k\)
-means
Clustering In Practice
Hierarchical Clustering
Gaussian Mixture Models
Classification
Learning From Data
Decision Trees
\(k\)
-Nearest Neighbors
Naive Bayes and Support Vector Machines
Dimensionality Reduction
Low Rank Approximation and the SVD
Dimensionality Reduction and PCA – SVD II
Regression
Linear Regression
Logistic Regression
Regularization
Selected Topics
Recommender Systems
Introduction to Networks
Network Centrality and Clustering
Gradient Descent
Index
