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 event”.

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

May 21st, 2021 30m 51s

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 broad

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

Jan 5th, 2021 33m 13s

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

How to Use Calculus to Design Learning Machines

Aug 29th, 2020 34m 22s

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

How to Analyze and Design Linear Machines

Jul 23rd, 2020 29m 5s

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 wide

How to Define Machine Learning (at at Least Try)

Apr 9th, 2020 37m 20s

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 form

How to Represent Knowledge using Set Theory

Feb 29th, 2020 31m 43s

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 rep

How to View Learning as Risk Minimization

Dec 24th, 2019 26m 7s

This particular podcast covers the material in Chapter 1 of my new (unpublished) book “Statistical Machine Learning: A unified framework”.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

How to become a machine learning expert

Oct 24th, 2019 39m 18s

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, softwar

How to Choose the Best Model using BIC

May 2nd, 2019 24m 15s

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 Informati

How to Choose the Best Model using AIC and GAIC

Jan 23rd, 2019 28m 17s

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. The pr

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

Dec 12th, 2018 36m 26s

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

How to Represent Knowledge using Logical Rules (remix)

Jun 30th, 2018 19m 22s

In this episode we will learn how to use “rules” to represent knowledge. We discuss how this works in practice and we explain how these ideas are implemented in a special architecture called the production system. The challenges of representing

How to Build a Machine that Learns to Play Checkers (remix)

Apr 25th, 2018 24m 58s

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 mix

Welcome to the Big Artificial Intelligence Magic Show!

Mar 31st, 2018 22m 7s

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 su

How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets

Feb 23rd, 2018 31m 40s

In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense into learning machines. Second, we discuss how first-order logic can be used to represent common sense know

How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding

Jan 31st, 2018 32m 4s

This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique called Stochastic Neighborhood Embedding. We discuss the concept of recognizing facial emotions in

What Happened at the 2017 Neural Information Processing Systems Conference?

Dec 16th, 2017 23m 20s

This 69th episode of Learning Machines 101 provides a short overview of the 2017 Neural Information Processing Systems conference with a focus on the development of methods for teaching learning machines rather than simply training them on exam

How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms

Sep 26th, 2017 21m 49s

This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithms which iteratively update their parameter vector by adding a perturbation based upon all of the trainin

LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)

Aug 21st, 2017 25m 40s

In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer the most probable values for unobservable variables. These constraints, however, can be learned from expe

How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)

Jul 17th, 2017 34m

In this episode of Learning Machines 101 (www.learningmachines101.com) we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints am

How to Design Gradient Descent Learning Machines (Rerun)

Jun 19th, 2017 30m

In this episode rerun we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of deep learning and neural network learning algorithms. Check out the website: www.learningmachines101.co

Stochastic Model Search and Selection with Genetic Algorithms (Rerun

May 15th, 2017 28m 4s

In this rerun of episode 24 we explore the concept of evolutionary learning machines. That is, learning machines that reproduce themselves in the hopes of evolving into more intelligent and smarter learning machines. This leads us to the topic

How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine

Apr 20th, 2017 22m 4s

This 63rd episode of Learning Machines 101 discusses how to build reinforcement learning machines which become smarter with experience but do not use this acquired knowledge to modify their actions and behaviors. This episode explains how to bu

How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine

Mar 19th, 2017 31m 5s

This 62nd episode of Learning Machines 101 (www.learningmachines101.com) discusses how to design reinforcement learning machines using your knowledge of how to build supervised learning machines! Specifically, we focus on Value Function Reinfo