LM101-079: Ch1: How to View Learning as Risk Minimization - Learning Machines 101

This particular podcast covers the material in Chapter 1 of my new (unpublished) book "Statistical Machine Learning: A unified framework". In this episode we discuss Chapter 1 of my new book, which shows how supervised, unsupervised, and reinforcement learning algorithms can be viewed as special cases of a general empirical risk minimization framework. This is useful because it provides a framework for not only understanding existing algorithms but also for suggesting new algorithms for specific applications. Welcome to the 79th podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence and machine learning in hopefully an entertaining and educational manner. This particular podcast is actually the second episode in a new special series of episodes designed to provide commentary on a new book that I am in the process of writing. The book's title is "Statistical Machine Learning: A unified framework" and it will be published by CRC Press in their "Texts in Statistical Science" series sometime in early 2021.