Learning Machines 101
Science
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!
Location:
Allen, TX
Description:
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!
Language:
English
Email:
rmgconsult@aol.com
Episodes
LM101-082: Ch4: How to Analyze and Design Linear Machines
7/23/2020
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. In addition, these same techniques are fundamentally important for the development of...
Duration:00:29:04
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
4/9/2020
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 formally define machine learning algorithms. Briefly, 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...
Duration:00:48:31
LM101-080: Ch2: How to Represent Knowledge using Set Theory
2/29/2020
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”.
Duration:00:41:31
LM101-079: Ch1: How to View Learning as Risk Minimization
12/23/2019
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 also for suggesting new algorithms for...
Duration:00:34:31
LM101-078: Ch0: How to Become a Machine Learning Expert
10/23/2019
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
Duration:00:39:17
LM101-077: How to Choose the Best Model using BIC
5/1/2019
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...
Duration:00:24:14
LM101-076: How to Choose the Best Model using AIC and GAIC
1/22/2019
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 precise semantic interpretation of these model selection criteria is 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. Briefly, AIC and...
Duration:00:28:16
LM101-075: Can computers think? A Mathematician's Response (remix)
12/12/2018
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. This episode is dedicated to the memory of my mom, Sandy Golden. To learn more about Turing Machines, SuperTuring Machines, Hypercomputation, and my Mom, check out:...
Duration:00:36:25
LM101-074: How to Represent Knowledge using Logical Rules (remix)
6/29/2018
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 knowledge using rules are also 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...
Duration:00:19:21
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
4/25/2018
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 mixture of classical artificial intelligence search methods and artificial neural network learning algorithms. The podcast ends with a book review of Professor Nilsson’s book: “The Quest for Artificial Intelligence: A History of Ideas and Achievements”....
Duration:00:24:57
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
3/30/2018
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 support the foundations of machine learning and artificial intelligence is discussed and the concept of the Big Artificial Intelligence Magic Show is introduced. At the end of the podcast, the book After Digital: Computation as Done by Brains and...
Duration:00:22:06
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
2/23/2018
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 knowledge. Third, we describe a large database of common sense knowledge where the knowledge is represented using first-order logic which is free for researchers in machine learning. We provide a hyperlink to this free database of common sense knowledge....
Duration:00:31:39
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
1/31/2018
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 images including applications to problems such as: improving online communication quality, identifying suspicious individuals such as terrorists using video cameras, improving lie detector tests, improving athletic performance by providing emotion...
Duration:00:32:03
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
12/16/2017
This 69 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 examples. In addition, a book review of the book “Deep Learning” is provided. #nips2017
Duration:00:23:19
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
9/25/2017
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 training data. This process is repeated, making a perturbation of the parameter vector based upon all of the training data until a parameter vector is generated which exhibits improved predictive performance. The magnitude of the perturbation at each learning...
Duration:00:21:48
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
8/21/2017
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 experience. Specifically, the important machine learning method for handling unobservable components of the data using Expectation Maximization is introduced. Check it out at:
www.learningmachines101.com
Duration:00:25:39
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
7/17/2017
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 among a collection of variables. The goal of the inference process is to infer the most probable values of the unobservable variables given the observable variables. Specifically, Monte Carlo Markov Chain ( MCMC ) methods are discussed.
Duration:00:33:59
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
6/19/2017
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.com
to obtain a transcript of this episode!
Duration:00:29:59
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
5/15/2017
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 of stochastic model search and evaluation. Check out the blog with additional technical references at: www.learningmachines101.com
Duration:00:28:03
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
4/20/2017
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 build reinforcement learning machines whose behavior evolves as the learning machines become increasingly smarter. The essential idea for the construction of such reinforcement learning machines is based upon first developing a supervised learning...
Duration:00:22:03