Module 1 - What is Machine Learning

Overview and Deliverables

• 8/31: Attend the initial course meetup at 6:45PM Eastern
• Due 9/06: Introduction Post in Brightspace Discussions
• Join Course Slack channel
• Install a suitable python IDE if you do not have one (for instance positron), and a basic python installation (see the software page)

Learning Objectives

• Examples of Machine Learning Problems
• Defining Supervised Learning, Unsupervised Learning, Reinforcement Learning
• Goals of Machine Learning: Prediction, Understanding (inference/causal inference), Decision Making
• The components of the machine learning problem
• Review course toolkit (or learn if unfamiliar): numpy, pandas, scikit-learn, matplotlib

Readings

• ISLP (Introduction to Statistical Learning): Chapter 1, Section 2.1

Interactive Lab

• ISLP (Introduction to Statistical Learning): [Section 2.3: Introduction to Python]

Videos

The book authors have a video playlist where they go through the book section by section. You may or may not like they style and presentation- my weekly videos will be different than theirs. I will also make videos of coding demonstrations. For this week the relevant links are:

Link to All Python Labs

Link to All Course Videos

Videos Covering Material Relevant to this week:

Opening Remarks Updated Opening Remarks Statistical Learning Examples and Framework 1.2 Statistical Learning Intro to Regression Models 2.1

Coding Videos Relevant to this week:

• Intro to the ISLP Presenters
• Setting Up Python
• Data Types, Arrays, and Basics
• Graphics
• Indexing and Data Frames