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 humanlike? These are the questions that will be addressed in this podcast series!

LM101081: Ch3: How to Define Machine Learning (or at Least Try)
This podcast covers the material in Chapter 3 of my new book “Statistical Machine Learning: A unified framework” which discusses how to formally define machine learning algorithms. A learning machine is viewed as a dynamical system that is minimizing an objective function. In addition, the knowledge structure of the learning machine is interpreted as a preference relation graph w implicitly specified by the objective function. Also, the new book “The Practioner’s Guide to Graph Data” is reviewe

LM101080: Ch2: How to Represent Knowledge using Set Theory
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 to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for Concept Modeling”.

LM101079: 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This particular podcast covers the material in Chapter 1 of my new (unpublished) book “Statistical Machine Learning: A unified framework”.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

LM101078: 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

LM101077: 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 outofsample 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.

LM101076: 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 crossvalidation methods.
Customer Reviews
An important resource for Machine Learning
Richard Golden provides an upbeat and positive introduction to Machine Learning. Whether you are a beginner in the field or an advanced practitioner, there is plenty to learn and enjoy in this podcast. Well done!
I’ll be back.
Very freaky, and it’s hard to follow along, it makes me feel very futile. And it was also very strange how I found out about this.they called didn’t answer me hung up and so I continued to call back and it said can’t not complete call at this time message LM 101. So I googled it “message LM101” and this came up. I’m just confused on why they/he/it went about reaching me this way.
Great for those interesting in machine learning.
This podcast is a great intoduction to the field of Machine Learning from statistics angle.