# Applied Data Science 2023

* 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>

[[file:slides/decision_trees.pdf][Decision trees]]

Lecture 02

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

[[file:slides/trees_ensembles.pdf][Trees: pruning and ensembles]]

with [[file:code/reg_trees.ipynb][code]]

Lecture 03

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

[[file:slides/pca.pdf][Dimensionality reduction: PCA]]

with [[file:code/pca.ipynb][code]]

Lecture 04

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

[[file:slides/non-linear_dimred.pdf][Dimensionality reduction: non-linear methods]]

with [[file:code/mnist.ipynb][code]]

Lecture 05

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

[[file:slides/k-means.pdf][Clustering: k-means]]

Lecture 06

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

[[file:slides/hierarchical-clustering.pdf][Clustering: hierarchical clustering]]

with [[file:code/clustering.ipynb][code]]

Lecture 07

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

[[file:slides/clustering_gmm.pdf][Clustering: GMMs and spectral clustering]]

Lecture 08

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

[[file:slides/clustering_density.pdf][Clustering: graph-based and density-based clustering]]

Lecture 09

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

[[file:slides/clustering_evaluation.pdf][Clustering evaluation, issues and outliers]]

* Reading 

[[https://www.statlearning.com][Introduction to Statistical Learning]] by James, Witten, Hastie, Tibshirani, Taylor.