Introduction

These are the notes for a set of 9 lectures, as part of the Applied Data Science course. The notes will cover supervised learning (decision trees) and unsupervised learning (dimension reduction, clustering).

Lectures

Lecture 01

<2023-11-08 Wed 12:00-13:00>

Decision trees

Lecture 02

<2023-11-09 Thu 10:00-11:00>

Trees: pruning and ensembles

with code

Lecture 03

<2023-11-13 Mon 11:00-12:00>

Dimensionality reduction: PCA

with code

Lecture 04

<2023-11-15 Wed 12:00-13:00>

Dimensionality reduction: non-linear methods

with code

Lecture 05

<2023-11-16 Thu 10:00-11:00>

Clustering: k-means

Lecture 06

<2023-11-20 Mon 11:00-12:00>

Clustering: hierarchical clustering

with code

Lecture 07

<2023-11-22 Wed 12:00-13:00>

Clustering: GMMs and spectral clustering

Lecture 08

<2023-11-23 Thu 10:00-11:00>

Clustering: graph-based and density-based clustering

Lecture 09

<2023-11-27 Mon 11:00-12:00>

Clustering evaluation, issues and outliers

Reading

Introduction to Statistical Learning by James, Witten, Hastie, Tibshirani, Taylor.