Toggle navigation sidebar

Toggle in-page Table of Contents

Introduction
to Machine Learning

Introduction to Machine Learning

About this course

Teaching philosophy
Instructor & Credits
Schedule
Tools
Evaluation

The basics

Warm-up: Linear Regression
Logistic Regression
Model Evaluation

Decision trees and boosting

Decision Trees
Ensemble Learning
- What is Ensemble Learning?
- Random Forests
Boosting

Neural Networks

Introduction
Motivations
Essential Concepts
Training Neural Networks
Optimizing Neural Networks
Train Your Neural Net!

Unsupervised Learning

Learning Without Labels?
Clustering: k-Means
Principal Component Analysis (PCA)
Autoencoders

STEP UP!

What is STEP?
- List of STEPs
- Submit Your Own STEP!

AI Ethics & Outlook

AI Ethics: What is it?
Outlook

Tutorial area

Setup
T1. Linear Regressor
T2. Classifier
T3. Decision Stump
T4. Forestry
T5. Neural Network by Hand!
T6. Unsupervised algorithms

Powered by Jupyter Book

.md

Clustering: k-Means

Clustering: k-Means#

Coming soon!

previous

Learning Without Labels?

next

Principal Component Analysis (PCA)

By Claire David • © 2025
CC BY-NC-SA 4.0