Category Archives: Machine Learning

LM101-082: Ch4: How to Analyze and Design Linear Machines

Episode Summary: This particular podcast covers the material in Chapter 4 of my new book “Statistical Machine Learning: A unified framework” which is now available! Many important and widely used machine learning algorithms may be interpreted as linear machines and this chapter shows how to use linear algebra to analyze and design such machines. In addition, these same… Read More »

LM101-077: How to Choose the Best Model using BIC

Episode Summary: In this episode, we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. Briefly, BIC is used to estimate the probability of the training data given the probability model, while AIC is used to estimate out-of-sample prediction… Read More »

LM101-073: How to Build a Machine that Learns Checkers (remix)

 LM101-073: How to Build a Machine that Learns Checkers (remix) Episode Summary: This is a remix of the original second episode Learning Machines 101 which describes in a little more detail how the computer program that Arthur Samuel developed in 1959 learned to play checkers by itself without human intervention using a mixture of classical artificial intelligence search… Read More »

LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix)

LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix) Episode Summary: This podcast is basically a remix of the first and second episodes of Learning Machines 101 and is intended to serve as the new introduction to the Learning Machines 101 podcast series. The search for common organizing principles which could support the foundations of machine… Read More »