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Tools for Data Science

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 and Model Selection
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

Neural Networks

Neural Networks I – Gradient Descent
NN II – Compute Graph, Backprop and Training
NN III – Stochastic Gradient Descent, Batches and Convolutional Neural Networks

Selected Topics

Recommender Systems
Introduction to Networks
Network Centrality and Clustering

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By Mark Crovella and Tom Gardos
© Copyright 2023.