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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

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Principal Component Analysis (PCA)

Principal Component Analysis (PCA)#

Coming soon!

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Clustering: k-Means

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Autoencoders

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