Introduction to Machine Learning in Chemistry

This repository contains two lectures and a workshop on introducing machine learning concepts in the advanced physical chemistry module at UoE.

Author

Dr Antonia Mey -- antonia.mey@ed.ac.uk

Accompanying Notebooks

Session	Materials
Clustering in scikit-learn
Regressions and Dimensionality Reduction

Summary

Lecture 1:

• What is machine learning?
• Examples of machine learning (in Chemistry)
• Introduction to unsupervised learning:
  • Clustering (k-means and others)
  • How does actual input data look like?
• Molecular fingerprints and nomenclature Introduction to supervised learning:
• What is a classification problem?

Lecture 2:

• Unsupervised learning continued:
  • Dimensionality reduction (PCA)
  • t-SNE
• Regressions
• Classifications in practice:
  • Random Forests
  • Multilayer perceptrons

Learning Outcomes

• Understand the main pillars of machine learning
• Know about different clustering techniques as part of unsupervised learning
• Be able to use common nomenclature used in machine learning
• Use Principle component analysis to reduce the dimensions of a data set
• Understand how a regression problem can be cast as a machine learning problem
• Be aware of how random forests and multilayer perceptrons can be used in a classification problem

Additional Resouces

A handout with additional resouces can be found here.