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Technology

Learning Machines 101

by Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
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Latest episodes

  • LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes

    July 20, 2021 · 35 min

    This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of outcomes in a space-time continuum which characterizes our physical world. Such a set is called an "environmental…

  • LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges

    May 21, 2021 · 31 min

    This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed to minimize smooth non-convex objective functions using batch learning methods. In particular, a…

  • LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems

    January 5, 2021 · 33 min

    In this episode of Learning Machines 101, we review Chapter 6 of my book "Statistical Machine Learning" which introduces methods for analyzing the behavior of machine inference algorithms and machine learning algorithms as dynamical…

  • How to Use Calculus to Design Learning Machines

    August 29, 2020 · 34 min

    This particular podcast covers the material from Chapter 5 of my new book "Statistical Machine Learning: A unified framework" which is now available! The book chapter shows how matrix calculus is very useful for the analysis and design of…

  • How to Analyze and Design Linear Machines

    July 23, 2020 · 29 min

    The main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled "Statistical Machine Learning: A unified framework."  Chapter 4 is titled "Linear Algebra for Machine Learning. Many important…

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  • How to Define Machine Learning (at at Least Try)

    April 9, 2020 · 37 min

    This particular podcast covers the material in Chapter 3 of my new book "Statistical Machine Learning: A unified framework" with expected publication date May 2020. In this episode we discuss Chapter 3 of my new book which discusses how to…

  • How to Represent Knowledge using Set Theory

    February 29, 2020 · 32 min

    This particular podcast covers the material in Chapter 2 of my new book "Statistical Machine Learning: A unified framework" with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how…

  • How to View Learning as Risk Minimization

    December 24, 2019 · 26 min

    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…

  • How to become a machine learning expert

    October 24, 2019 · 39 min

    This particular podcast (Episode 78 of Learning Machines 101) is the initial episode in a new special series of episodes designed to provide commentary on a new book that I am in the process of writing. In this episode we discuss books…

  • How to Choose the Best Model using BIC

    May 2, 2019 · 24 min

    In this 77th episode of www.learningmachines101.com , we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike…

  • How to Choose the Best Model using AIC and GAIC

    January 23, 2019 · 28 min

    In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Information Criterion (GAIC) for the purpose of picking the best model for a given set of training data…

  • Can computers think? A Mathematician's Response (remix)

    December 12, 2018 · 36 min

    In this episode, we explore the question of what can computers do as well as what computers can't do using the Turing Machine argument. Specifically, we discuss the computational limits of computers and raise the question of whether such…

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About the show

Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions that will be addressed in this podcast series!