Machine Learning

In this workshop we will finally get our hands dirty with some Machine Learning using scikit-learn!

Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. (Documentation: https://scikit-learn.org/stable/index.html)

The following topics will be covered:

Machine Learning:

• Supervised vs Unsupervised Learning
• Training, Validation and Test
• Overfitting vs Underfitting
• Models
  • k-Nearest Neighbours
    • you can 'try' out KNN interactively on the following pages:
      https://observablehq.com/@antoinebrl/knn-visualizing-the-variance
      http://vision.stanford.edu/teaching/cs231n-demos/knn/
  • Decision Tree

scikit-learn:

• Import and use standard models on an example dataset
  
  • KNeighborsClassifier
  • DecisionTreeClassifier