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!

LM101084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
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 systems. We show that when dynamical systems can be viewed as special types of optimization algorithms, the behavior of those systems even when they are highly nonlinear and highdimensional can be analyzed.

How to Use Calculus to Design Learning Machines
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 both linear and nonlinear learning machines with lots of examples. We discuss the relevance of the matrix chain rule and matrix Taylor series for machine learning algorithm design and the analysis of generalization performance! Check out: www.learningmachines101.com

How to Analyze and Design Linear Machines
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 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. Check out: www.statisticalmachinelearning.com 
How to Define Machine Learning (at 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

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”.

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