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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

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By Mark Crovella

© Copyright 2021-2022.