Machine learning paradigms and workflows

Overview of ML paradigms and workflows in R

Author

Affiliation

Stephanie Hicks

Department of Biostatistics, Johns Hopkins

Pre-lecture activities

No Pre-lecture readings today! Just focus on Project 2 and Project 4 (Part 2) – both due tomorrow (Friday November 15th at 11:59pm).

Lecture

Acknowledgements

Material for this lecture was borrowed and adopted from

• An Introduction to Statistical Learning

Learning objectives

Learning objectives

At the end of this lesson you will:

• State the differences between machine learning paradigms including supervised, unsupervised, semi-supervised, and reinforcement Learning
• Describe some common methods for each of the ML paradigms
• Describe some evaluation techniques

Slides

• Lecture 06: Machine learning paradgims

Class activity

For the rest of the time in class, you and your team will work on the final project. Stephanie will be on zoom to answer questions and happy to help in anyway!