• What is Machine Learning?
• What are the main categories of Machine Learning?
• What are some examples of Machine Learning?
• How does Machine Learning “work”?

One definition: “Machine Learning is the semi-automated extraction of knowledge from data”

• Knowledge from data: Starts with a question that might be answerable using data
• Automated extraction: A computer provides the insight
• Semi-automated: Requires many smart decisions by a human

Supervised learning: Making predictions using data

Unsupervised learning: Extracting structure from data

High-level steps of supervised learning:

1. First, train a Machine Learning model using labeled data

   • “Labeled data” has been labeled with the outcome
   • “Machine Learning model” learns the relationship between the attributes of the data and its outcome

2. Then, make predictions on new data for which the label is unknown

The primary goal of supervised learning is to build a model that “generalizes”

The goal is to accurately predict the future rather than the past!

• How do I choose which attributes of my data to include in the model?
• How do I choose which model to use?
• How do I optimize this model for best performance?
• How do I ensure that I’m building a model that will generalize to unseen data?
• Can I estimate how well my model is likely to perform on unseen data?

• Book: An Introduction to Statistical Learning (section 2.1, 14 pages)
• Video: Learning Paradigms (13 minutes)