Course Contents#

  1. Introduction

    • Overview

    • Expectations and assessments

    • Exercise: Getting started

  2. Machine Learning Basics

    • Terminology

    • Learning by example

      • Supervised

      • Unsupervised

      • Reinforcement

    • Exercise: Crystal hardness

  3. Materials Data

    • Data sources and formats

    • API queries

    • Exercise: Data-driven thermoelectrics

  4. Crystal Representations

    • Compositional

    • Structural

    • Graphs

    • Exercise: Crystal space

  5. Classical Learning

    • k-nearest neighbours

    • k-means clustering

    • Decision trees and beyond

    • Exercise: Metal or insulator?

  6. Artificial Neural Networks

    • From neuron to perceptron

    • Network architecture and training

    • Convolutional neural networks

    • Exercise: Learning microstructure

  7. Building a Model from Scratch

    • Data preparation

    • Model choice

    • Training and testing

    • Exercise: Crystal hardness II

  8. Accelerated Discovery

    • Automated experiments

    • Bayesian optimisation

    • Reinforcement learning

    • Exercise: Closed-loop optimisation

  9. Generative Artificial Intelligence

    • Large language models

    • From latent space to diffusion

    • Exercise: Research challenge

  10. Recent Advances

    • Guest lecture

    • Exercise: Research challenge