78 episodes

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!

Learning Machines 101 Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.

    • Technology

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!

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

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

    This particular podcast covers the material in Chapter 1 of my new (unpublished) book “Statistical Machine Learning: A unified framework”.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 for suggesting new algorithms for specific applications

    • 26 min
    LM101-078: Ch0: How to Become a Machine Learning Expert

    LM101-078: Ch0: How to Become a Machine Learning Expert

    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, software, courses, and podcasts designed to help you become a machine learning expert! For more information, check out: www.learningmachines101.com

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

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

    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 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 error. The probability of the training data given the model is called the “marginal likelihood”.  Using the marginal likelihood, one can calculate the probability of a model given the training data and then use this analysis to support selecting the most probable model, selecting a model that minimizes expected risk, and support Bayesian model averaging. The assumptions which are required for BIC to be a valid approximation for the probability of the training data given the probability model are also discussed.

    • 24 min
    LM101-076: How to Choose the Best Model using AIC and GAIC

    LM101-076: How to Choose the Best Model using AIC and GAIC

    The precise semantic interpretation of the Akaike Information Criterion (AIC) and Generalized Akaike Information Criterion (GAIC) for selecting the best model are provided, explicit assumptions are provided for the AIC and GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be used in practice. AIC and GAIC provide a way of estimating the average prediction error of your learning machine on test data without using test data or cross-validation methods.

    • 28 min
    LM101-075: Can computers think? A Mathematician's Response (remix)

    LM101-075: Can computers think? A Mathematician's Response (remix)

    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 limits pertain to biological brains and other non-standard computing machines.

    • 36 min
    LM101-074: How to Represent Knowledge using Logical Rules (remix)

    LM101-074: How to Represent Knowledge using Logical Rules (remix)

    The challenges of representing knowledge using rules are discussed. Specifically, these challenges include: issues of feature representation, having an adequate number of rules, obtaining rules that are not inconsistent, and having rules that handle special cases and situations. To learn more, visit:

    www.learningmachines101.com

    • 19 min

Customer Reviews

yoda108 ,

Great podcast!

Very interesting

Claude Coulombe ,

Not afraid to go in depth if required...

Learning Machines 101 is a « small jewel » which I listen to carefully while I'm on public transit. The host, Dr Richard Golden, is a passionated pioneer in the AI field with a great radio voice. Dr Golden is not afraid to go in depth with always a desire to be informative, at the risk sometimes of talking about maths and algorithmms when it is required and even repeat important concepts. Furthermore, the companion website with transcriptions, figures, formulas and URLs resources is a valuable complement to the audio podcast. I recommend this podcast to anyone interested to learn or refresh his knowledge of machine learning in an agreable and didactic fashion.

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