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Episode  Date 

LM101076: How to Choose the Best Model using AIC and GAIC
28:17
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 GAIC provide a way of estimating the average prediction error of your learning machine on test data without using test data or crossvalidation methods. The GAIC is also called the Takeuchi Information Criterion (TIC). 
Jan 23, 2019 
LM101075: Can computers think? A Mathematician's Response (remix)
36:26
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 nonstandard 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: www.learningmachines101.com 
Dec 12, 2018 
LM101074: How to Represent Knowledge using Logical Rules (remix)
19:22
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 situations. To learn more, visit:

Jun 30, 2018 
LM101073: How to Build a Machine that Learns to Play Checkers (remix)
24:58
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”. For more information, check out:

Apr 25, 2018 
LM101072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101001 and LM101002)
22:07
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 Machines by Professor James A. Anderson is briefly reviewed. For more information, please visit: www.learningmachines101.com

Mar 31, 2018 
LM101071: How to Model Common Sense Knowledge using FirstOrder Logic and Markov Logic Nets
31:40
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 firstorder 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 firstorder logic which is free for researchers in machine learning. We provide a hyperlink to this free database of common sense knowledge. Fourth, we discuss some problems of firstorder logic and explain how these problems can be resolved by transforming logical rules into probabilistic rules using Markov Logic Nets. And finally, we have another book review of the book “Markov Logic: An Interface Layer for Artificial Intelligence” by Pedro Domingos and Daniel Lowd which provides further discussion of the issues in this podcast. In this book review, we cover some additional important applications of Markov Logic Nets not covered in detail in this podcast such as: object labeling, social network link analysis, information extraction, and helping support robot navigation. Finally, at the end of the podcast we provide information about a free software program which you can use to build and evaluate your own Markov Logic Net! For more information check out: www.learningmachines101.com

Feb 23, 2018 
LM101070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
32:04
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 feedback, and designing smart advertising which can look at the customer’s face to determine if they are bored or interested and dynamically adapt the advertising accordingly. To address this problem we review clustering algorithm methods including Kmeans clustering, Linear Discriminant Analysis, Spectral Clustering, and the relatively new technique of Stochastic Neighborhood Embedding (SNE) clustering. At the end of this podcast we provide a brief review of the classic machine learning text by Christopher Bishop titled “Pattern Recognition and Machine Learning”. Make sure to visit: www.learningmachines101.com 
Jan 31, 2018 
LM101069: What Happened at the 2017 Neural Information Processing Systems Conference?
23:20
This 69^{th} 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 
Dec 16, 2017 
LM101068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
21:49
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 iteration is called the “stepsize” or “learning rate” and the identity of the perturbation vector is called the “search direction”. Simple mathematical formulas are presented based upon research from the late 1960s by Philip Wolfe and G. Zoutendijk that ensure convergence of the generated sequence of parameter vectors. These formulas may be used as the basis for the design of artificially intelligent smart automatic learning rate selection algorithms. For more information, please visit the official website:

Sep 26, 2017 
LM101067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
25:40
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:

Aug 21, 2017 
LM101066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
34:00
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. 
Jul 17, 2017 
LM101065: How to Design Gradient Descent Learning Machines (Rerun)
30:00
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: to obtain a transcript of this episode! 
Jun 19, 2017 
LM101064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
28:04
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 
May 15, 2017 
LM101063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
22:04
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 machine. The supervised learning machine then “guesses” the desired response and updates its parameters using its guess for the desired response! Although the reasoning seems circular, this approach in fact is a variation of the important widely used machine learning method of ExpectationMaximization. Some applications to learning to play video games, control walking robots, and developing optimal trading strategies for the stock market are briefly mentioned as well. Check us out at: www.learningmachines101.com

Apr 20, 2017 
LM101062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
31:05
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 Reinforcement Learning Machines which estimate the unobservable total penalty associated with an episode when only the beginning of the episode is observable. This estimated Value Function can then be used by the learning machine to select a particular action in a given situation to minimize the total future penalties that will be received. Applications include: building your own robot, building your own automatic aircraft lander, building your own automated stock market trading system, and building your own selfdriving car!! 
Mar 19, 2017 
LM101061: What happened at the Reinforcement Learning Tutorial? (RERUN)
29:15
This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode reviews and discusses topics associated with the Introduction to Reinforcement Learning with Function Approximation Tutorial presented by Professor Richard Sutton on the first day of the conference. This episode is a RERUN of an episode originally presented in January 2016 and lays the groundwork for future episodes on the topic of reinforcement learning! Check out: www.learningmachines101.com for more info!! 
Feb 23, 2017 
LM101060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
29:32
This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to monitor the performance of other machine learning algorithms deployed in real world environments. The episode is based upon a review of a talk by Chief Data Scientist Ira Cohen of Anodot presented at the 2016 Berlin Buzzwords Data Science Conference. Check out: www.learningmachines101.com to hear the podcast or read a transcription of the podcast! 
Jan 23, 2017 
LM101059: How to Properly Introduce a Neural Network
29:56
I discuss the concept of a “neural network” by providing some examples of recent successes in neural network machine learning algorithms and providing a historical perspective on the evolution of the neural network concept from its biological origins. For more details visit us at: www.learningmachines101.com

Dec 21, 2016 
LM101058: How to Identify Hallucinating Learning Machines using Specification Analysis
19:38
In this 58th episode of Learning Machines 101, I’ll be discussing an important new scientific breakthrough published just last week for the first time in the journal Econometrics in the special issue on model misspecification titled “Generalized Information Matrix Tests for Detecting Model Misspecification”. The article provides a unified theoretical framework for the development of a wide range of methods for determining if a learning machine is capable of learning its statistical environment. The article is coauthored by myself, Steven Henley, Halbert White, and Michael Kashner. It is an openaccess article so the complete article can be downloaded for free! The download link can be found in the show notes of this episode at: www.learningmachines101.com . In 30 years everyone will be using these methods so you might as well start using them now! 
Nov 23, 2016 
LM101057: How to Catch Spammers using Spectral Clustering
19:54
In this 57th episode, we explain how to use unsupervised machine learning algorithms to catch internet criminals who try to steal your money electronically! Check it out at: www.learningmachines101.com

Oct 18, 2016 
LM101056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
27:59
In this NEW episode we discuss Latent Semantic Indexing type machine learning algorithms which have a PROBABILISTIC interpretation. We explain why such a probabilistic interpretation is important and discuss how such algorithms can be used in the design of document retrieval systems, search engines, and recommender systems. Check us out at: www.learningmachines101.com and follow us on twitter at: @lm101talk

Sep 20, 2016 
LM101055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
35:06
In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that can use prior knowledge to facilitate and guide the learning of statistical regularities. In particular, the episode introduces fundamental machine learning concepts such as: probability models, model misspecification, maximum likelihood estimation, and MAP estimation. Check us out at: www.learningmachines101.com

Aug 16, 2016 
LM101054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
29:35
Welcome to the 54th Episode of Learning Machines 101 titled "How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis" (rerun of Episode 40). The principles in this episode are also applicable to the problem of "Market Basket Analysis" and the design of Recommender Systems. Check it out at: www.learningmachines101.com and follow us on twitter: @lm101talk 
Jul 25, 2016 
LM101053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
26:50
In this 53rd episode of Learning Machines 101, we introduce the concept of a Swarm Intelligence with respect to Particle Swarm Optimization Algorithms. The essential idea of “Swarm Intelligence” is that you have a group of individual entities which behave in a coordinated manner yet there is no master control center providing directions to all of the individuals in the group. The global group behavior is an “emergent property” of local interactions among individuals in the group! We will analyze the concept of swarm intelligence as a Markov Random Field, discuss how it can be harnessed to enhance the performance of machine learning algorithms, and comment upon relevant mathematics for analyzing and designing “swarm intelligences” so they behave in an appropriate manner by viewing the Swarm as a nonlinear optimization algorithm. For more information check out: www.learningmachines101.com and also check us out on twitter (@lm101talk). 
Jul 11, 2016 
LM101052: How to Use the Kernel Trick to Make Hidden Units Disappear
28:57
Today, we discuss a simple yet powerful idea which began popular in the machine learning literature in the 1990s which is called “The Kernel Trick”. The basic idea of the “Kernel Trick” is that you specify similarity relationships among input patterns rather than a recoding transformation to solve a nonlinear problem with a linear learning machine. It's a great magic trick...check it out at: www.learningmachines101.com where you can obtain transcripts of this episode and download free machine learning software! Also check out the "Statistical Machine Learning Forum" on Linked In and follow us on Twitter using the twitter handle: @lm101talk

Jun 13, 2016 
LM101051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
29:04
This particular podcast is a RERUN of Episode 20 and describes step by step how to download free software which can be used to make predictions using a feedforward artificial neural network whose hidden units are radial basis functions. This is essentially a nonlinear regression modeling problem. We show the performance of this nonlinear learning machine is substantially better on test data set than the linear learning machine software presented in Episode 13. Basically performance for the linear learning machine was about 13% because the data set was specifically designed to be unlearnable by a linear learning machine, while the performance for the nonlinear machine learning software in this episode is about 70%. Again, I'm a little disappointed that only a few people have downloaded the software and tried things out. You can download windows executable, mac executable, or the MATLAB source code. It's important to actually experiment with real machine learning software if you want to learn about machine learning! Check out: www.learningmachines101.com Or tweet us at: @lm101talk

May 24, 2016 
LM101050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
30:32
In this episode we will explain how to download and use free
machine learning software from the website: www.learningmachines101.com.
This podcast is concerned with the very practical issues
associated with downloading and installing machine learning
software on your computer. If you follow these instructions, by the
end of this episode you will have installed one of the simplest
(yet most widely used) machine learning algorithms on your
computer. You can then use the software to make virtually any kind
of prediction you like. Also follow us on

May 04, 2016 
LM101049: How to Experiment with Lunar Lander Software
34:40
In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We describe how to download free lunar lander software so you can experiment with an autopilot for a lunar lander module that learns from its experiences and describe the results of some simulation studies. To learn more, visit: to download the free lunar lander software which illustrates principles of temporal reinforcement learning and nonlinear control theory. You will also have the opportunity to download free software which illustrates how a simple deep learning neural network with one layer of radial basis functions works and a simple linear regression model learning machine. Check it out!!! 
Apr 22, 2016 
LM101048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
31:27
In this episode we consider the problem of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We illustrate the solution to this problem by designing an autopilot for a lunar lander module that learns from its experiences. For more information, check out: and visit us a twitter: @lm101talk #machinelearning #statistics #artificialintelligence 
Mar 29, 2016 
LM101047: How Build a Support Vector Machine to Classify Patterns (Rerun)
35:29
We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories as well as identify special pattern vectors called “support vectors” which are important for characterizing the Support Vector Machine decision boundary. The relationship of Support Vector Machine parameter estimation and logistic regression parameter estimation is also discussed. also check us out on twitter at: lm101talk

Mar 14, 2016 
LM101046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
23:19
In this episode, we briefly review Item Response Theory and Bayesian Network Theory methods for the assessment and optimization of student learning and then describe a poster presented on the first day of the Neural Information Processing Systems conference in December 2015 in Montreal which describes a Recurrent Neural Network approach for the assessment and optimization of student learning called “Deep Knowledge Tracing”. For more details check out: www.learningmachines101.com and follow us on Twitter at: @lm101talk

Feb 23, 2016 
LM101045: How to Build a Deep Learning Machine for Answering Questions about Images
21:51
In this episode we discuss just one out of the 102 different posters which was presented on the first night of the 2015 Neural Information Processing Systems Conference. This presentation describes a system which can answer simple questions about images. Check out: www.learningmachines101.com for additional details!! 
Feb 08, 2016 
LM101044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
31:38
This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode reviews and discusses topics associated with the Introduction to Reinforcement Learning with Function Approximation Tutorial presented by Professor Richard Sutton on the first day of the conference. Check out: www.learningmachines101.com to learn more!! Also follow us at: "lm101talk" on twitter!

Jan 26, 2016 
LM101043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
27:38
Welcome to the 43rd Episode of Learning Machines 101! 
Jan 12, 2016 
LM101042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
25:46
This is the second of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode reviews and discusses topics associated with the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial held on the first day of the conference. Check out: www.learningmachines101.com to listen or download this podcast episode or download the transcripts! Also visit us at LINKEDIN or TWITTER. The twitter handle is: LM101TALK

Dec 29, 2015 
LM101041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
29:38
This is the first of a short subsequence of podcasts which provides a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode introduces the Neural Information Processing Systems Conference and reviews the content of the Morning Deep Learning Tutorial which took place on the first day of the conference. Check out: www.learningmachines101.com 
Dec 16, 2015 
LM101040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
28:15
In this episode we introduce a very powerful approach for computing semantic similarity between documents. Here, the terminology “document” could refer to a webpage, a word document, a paragraph of text, an essay, a sentence, or even just a single word. Two semantically similar documents, therefore, will discuss many of the same topics while two semantically different documents will not have many topics in common. Machine learning methods are described which can take as input large collections of documents and use those documents to automatically learn semantic similarity relations. This approach is called Latent Semantic Indexing (LSI) or Latent Semantic Analysis (LSA). Visit us at: www.learningmachines101.com to learn more! 
Nov 24, 2015 
LM101039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
35:17
In this episode 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. Concepts of Markov Random Fields and Monte Carlo Markov Chain methods are discussed. For additional details and technical notes, please visit the website: www.learningmachines101.com Also feel free to visit us at twitter: @lm101talk

Nov 09, 2015 
LM101038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets
23:55
In this episode, we examine the problem of developing an advanced artificially intelligent technology which is capable of tracking knowledge growth in students in realtime, representing the knowledge state of a student a skill profile, and automatically defining the concept of a skill without human intervention! The approach can be viewed as a sophisticated stateoftheart extension of the Item Response Theory approach to Computerized Adaptive Testing Educational Technology described in Episode 37. Both tutorial notes and advanced implementational notes can be found in the show notes at: www.learningmachines101.com. 
Oct 27, 2015 
LM101037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory
34:56
In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). Suppose that you are teaching a student a particular target set of knowledge. Examples of such situations obviously occur in nursery school, elementary school, junior high school, high school, and college. However, such situations also occur in industry when top professionals in a particular field attend an advanced training seminar. All of these situations would benefit from a smart adaptive assessment machine which attempts to estimate a student’s knowledge in realtime. Such a machine could then use that information to optimize the choice and order of questions to be presented to the student in order to develop a customized exam for efficiently assessing the student’s knowledge level and possibly guiding instructional strategies. Both tutorial notes and advanced implementational notes can be found in the show notes at: www.learningmachines101.com . 
Oct 12, 2015 
LM101036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
25:26
In this episode, we discuss the problem of predicting the future from not only recent events but also from the distant past using Recurrent Neural Networks (RNNs). A example RNN is described which learns to label images with simple sentences. A learning machine capable of generating even simple descriptions of images such as these could be used to help the blind interpret images, provide assistance to children and adults in language acquisition, support internet search of content in images, and enhance search engine optimization websites containing unlabeled images. Both tutorial notes and advanced implementational notes for RNNs can be found in the show notes at: www.learningmachines101.com . 
Sep 28, 2015 
LM101035: What is a Neural Network and What is a Hot Dog?
28:59
In this episode, we address the important questions of “What is a neural network?” and “What is a hot dog?” by discussing human brains, neural networks that learn to play Atari video games, and rat brain neural networks. Check out: See you soon!!

Sep 15, 2015 
LM101034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
29:04
Welcome to the 34th podcast in the podcast series Learning Machines 101 titled "How to Use Nonlinear Machine Learning Software to Make Predictions". This particular podcast is a RERUN of Episode 20 and describes step by step how to download free software which can be used to make predictions using a feedforward artificial neural network whose hidden units are radial basis functions. This is essentially a nonlinear regression modeling problem. Check out: www.learningmachines101.com 
Aug 25, 2015 
LM101033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
31:28
In this episode will explain how to download and use free machine learning software which can be downloaded from the website: www.learningmachines101.com. The software can be used to make predictions using your own data sets. Although we will continue to focus on critical theoretical concepts in machine learning in future episodes, it is always useful to actually experience how these concepts work in practice.This is a rerun of Episode 13. 
Aug 10, 2015 
LM101032: How To Build a Support Vector Machine to Classify Patterns
35:25
In this 32^{nd} episode of Learning Machines 101, we introduce the concept of a Support Vector Machine. We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories as well as identify special pattern vectors called “support vectors” which are important for characterizing the Support Vector Machine decision boundary. The relationship of Support Vector Machine parameter estimation and logistic regression parameter estimation is also discussed. Check out this and other episodes as well as supplemental references to these episodes at the website: www.learningmachines101.com. Also follow us at twitter using the twitter handle: lm101talk. 
Jul 13, 2015 
LM101031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)
32:02
In this rerun of Episode 16, we introduce the important concept of gradient descent which is the fundamental principle underlying learning mechanisms in a wide range of machine learning algorithms. Check out the transcripts of this episode and related references and software at: www.learningmachines101.com !!! 
Jun 21, 2015 
LM101030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
32:02
Deep learning machine technology has rapidly developed over the past five years due in part to a variety of actors such as: better technology, convolutional net algorithms, rectified linear units, and a relatively new learning strategy called "dropout" in which hidden unit feature detectors are temporarily deleted during the learning process. This article introduces and discusses the concept of "dropout" to support deep learning performance and makes connections of the "dropout" concept to concepts of regularization and model averaging. For more details and background references, check out: www.learningmachines101.com !

Jun 08, 2015 
LM101029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and MaxPooling
35:59
This podcast discusses talks, papers, and ideas presented at the recent International Conference on Learning Representations 2015 which was followed by the Artificial Intelligence in Statistics 2015 Conference in San Diego. Specifically, commonly used techniques shared by many successful deep learning algorithms such as: rectilinear units, convolutional filters, and maxpooling are discussed. For more details please visit our website at: www.learningmachines101.com! 
May 25, 2015 
LM101028: How to Evaluate the Ability to Generalize from Experience (CrossValidation Methods)[RERUN]
35:06
This rerun of an earlier episode of Learning Machines 101 discusses the problem of how to evaluate the ability of a learning machine to make generalizations and construct abstractions given the learning machine is provided a finite limited collection of experiences. Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software! 
May 11, 2015 
LM101027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]
39:42
In this episode of Learning Machines 101 we discuss the design of statistical learning machines which can make inferences about rare and unseen events using prior knowledge. Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software! 
Apr 28, 2015 
LM101026: How to Learn Statistical Regularities (Rerun)
35:12
In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that can use prior knowledge to facilitate and guide the learning of statistical regularities. The topics of ML (Maximum Likelihood) and MAP (Maximum A Posteriori) estimation are discussed in the context of the nature versus nature problem. Check out: www.learningmachines101.com to obtain transcripts of this podcast 
Apr 14, 2015 
LM101025: How to Build a Lunar Lander Autopilot Learning Machine
31:29
In this episode we consider the problem of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We illustrate the solution to this problem by designing an autopilot for a lunar lander module that learns from its experiences!
Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

Mar 24, 2015 
LM101024: How to Use Genetic Algorithms to Breed Learning Machines
29:15
In this episode we introduce the concept of learning machines that can selfevolve using simulated natural evolution into more intelligent machines using Monte Carlo Markov Chain Genetic Algorithms. Check out: to obtain transcripts of this podcast and download free machine learning software! 
Mar 10, 2015 
LM101023: How to Build a Deep Learning Machine
42:45
Recently, there has been a lot of discussion and controversy over the currently hot topic of “deep learning”!! Deep Learning technology has made real and important fundamental contributions to the development of machine learning algorithms. Learn more about the essential ideas of "Deep Learning" in Episode 23 of "Learning Machines 101". Check us out at our official website: www.learningmachines101.com ! 
Feb 24, 2015 
LM101022: How to Learn to Solve Large Constraint Satisfaction Problems
26:49
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. At the end of the episode, we discuss one (unproven) theory from the field of neuroscience that our "dreams" are actually neural simulations of variations of events we have experienced during the day and "unlearning" of these dreams helps us to organize our memory! Visit us at: www.learningmachines101.com to obtain additional references, make suggestions regarding topics for future podcast episodes by joining the learning machines 101 community, and download free machine learning software! 
Feb 10, 2015 
LM101021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
35:11
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. Please visit: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software! 
Jan 26, 2015 
LM101020: How to Use Nonlinear Machine Learning Software to Make Predictions
27:21
In this episode we introduce some advanced nonlinear machine software which is more complex and powerful than the linear machine software introduced in Episode 13. Specifically, the software implements a multilayer nonlinear learning machine, however, whose inputs feed into hidden units which in turn feed into output units has the potential to learn a much larger class of statistical environments. Download the free software from: www.learningmachines101.com now! 
Jan 12, 2015 
LM101019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
36:13
Embodied cognition emphasizes the design of complex artificially intelligent systems may be both vastly simplified and vastly enhanced if we view the robotic bodies of artificially intelligent systems as important contributors to intelligent behavior. Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software! 
Dec 22, 2014 
LM101018: Can Computers Think? A Mathematician's Response (Rerun)
36:40
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 nonstandard computing machines. This is a rerun of Episode 4. We continue new podcasts in January 2015! For a transcript of this episode, please visit our website: www.learningmachines101.com!!!

Dec 12, 2014 
LM101017: How to Decide if a Machine is Artificially Intelligent (Rerun)
33:40
This episode we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable from the behavior of a thinking human being. The chatbot A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) is interviewed and basic concepts of AIML (Artificial Intelligence Markup Language) are introduced. 
Nov 24, 2014 
LM101016: How to Analyze and Design Learning Rules using Gradient Descent Methods
31:21
In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learning algorithms. For more podcast episodes on the topic of machine learning and free machine learning software, please visit us at: www.learningmachines101.com !! 
Nov 11, 2014 
LM101015: How to Build a Machine that Can Learn Anything (The Perceptron)
30:07
In this 15th episode of Learning Machines 101, we discuss the problem of how to build a machine that can learn any given pattern of inputs and generate any desired pattern of outputs when it is possible to do so! It is assumed that the input patterns consists of zeros and ones indicating possibly the presence or absence of a feature. Check out: www.learningmachines101.com 
Oct 27, 2014 
LM101014: How to Build a Machine that Can Do Anything (Function Approximation)
32:36
In this episode, we discuss the problem of how to build a machine that can do anything! Or more specifically, given a set of input patterns to the machine and a set of desired output patterns for those input patterns we would like to build a machine that can generate the specified output pattern for a given input pattern. This problem may be interpreted as an example of solving a supervised learning problem. Checkout the shownotes at: www.learningmachines101.com 
Oct 13, 2014 
LM101013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)
30:35
Hello everyone! Welcome to the thirteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence and machine learning in hopefully an entertaining and educational manner.
In this episode we will explain how to download and use free machine learning software which can be downloaded from the website: www.learningmachines101.com. Although we will continue to focus on critical theoretical concepts in machine learning in future episodes, it is always useful to actually experience how these concepts work in practice. For these reasons, from time to time I will include special podcasts like this one which focus on very practical issues associated with downloading and installing machine learning software on your computer. If you follow these instructions, by the end of this episode you will have installed one of the simplest (yet most widely used) machine learning algorithms on your computer. You can then use the software to make virtually any kind of prediction you like. However, some of these predictions will be good predictions, while other predictions will be poor predictions. For this reason, following the discussion in Episode 12 which was concerned with the problem of evaluating generalization performance, we will also discuss how to evaluate what your learning machine has “memorized” and additionally evaluate the ability of your learning machine to “generalize” and make predictions about things that it has never seen before. 
Sep 22, 2014 
LM101012: How to Evaluate the Ability to Generalize from Experience (CrossValidation Methods)
32:50
In this episode we discuss the problem of how to evaluate the ability of a learning machine to make generalizations and construct abstractions given the learning machine is provided a finite limited collection of experiences. 
Sep 09, 2014 
LM101008: How to Represent Beliefs Using Probability Theory
30:41
Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probability theory is introduced as a special case of fuzzy measure theory which is consistent with classical laws of logical inference. 
Sep 03, 2014 
LM101011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)
40:23
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: Today we address a strange yet fundamentally important question. How do you predict the probability of something you have never seen? Or, in other words, how can we accurately estimate the probability of rare events? Show Notes: Hello everyone! Welcome to the eleventh podcast in the podcast series Learning Machines 101. In this series of podcasts. Read More » The post LM101011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws) appeared first on Learning Machines 101. 
Aug 26, 2014 
LM101010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)
34:34
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this podcast episode, we discuss fundamental principles of learning in statistical environments including the design of learning machines that can use prior knowledge to facilitate and guide the learning of statistical regularities. Show Notes: Hello everyone! Welcome to the tenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal. Read More » The post LM101010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation) appeared first on Learning Machines 101. 
Aug 12, 2014 
LM101009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
35:21
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: Embodied cognition emphasizes the design of complex artificially intelligent systems may be both vastly simplified and vastly enhanced if we view the robotic bodies of artificially intelligent systems as important contributors to intelligent behavior. Show Notes: Hello everyone! Welcome to the ninth podcast in the podcast series Learning Machines 101. In this series of podcasts my. Read More » The post LM101009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition) appeared first on Learning Machines 101. 
Jul 28, 2014 
LM101007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory
26:33
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In real life, there is no certainty. There are always exceptions. In this episode, two methods are discussed for making inferences in uncertain environments. In fuzzy set theory, a smart machine has certain beliefs about imprecisely defined concepts. In fuzzy measure theory, a smart machine has beliefs about precisely defined concepts but some beliefs are stronger. Read More » The post LM101007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory appeared first on Learning Machines 101. 
Jun 23, 2014 
LM101006: How to Interpret Turing Test Results
31:24
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this episode, we briefly review the concept of the Turing Test for Artificial Intelligence (AI) which states that if a computer.s behavior is indistinguishable from that of the behavior of a thinking human being, then the computer should be called .artificially intelligent.. Some objections to this definition of artificial intelligence are introduced and discussed. At. Read More » The post LM101006: How to Interpret Turing Test Results appeared first on Learning Machines 101. 
Jun 09, 2014 
LM101005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)
32:06
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: This episode we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable from the behavior of a thinking human being. The chatbot A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) is interviewed and basic concepts of AIML (Artificial Intelligence Markup Language) are introduced. Show Notes: Hello everyone!. Read More » The post LM101005: How to Decide if a Machine is Artificially Intelligent (The Turing Test) appeared first on Learning Machines 101. 
May 27, 2014 
LM101004: Can computers think? A mathematician.s response
34:26
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: 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 nonstandard computing machines. Show Notes: Hello everyone! Welcome to the. Read More » The post LM101004: Can computers think? A mathematician.s response appeared first on Learning Machines 101. 
May 12, 2014 
LM101003: How to Represent Knowledge using Logical Rules
20:19
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: 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. Read More » The post LM101003: How to Represent Knowledge using Logical Rules appeared first on Learning Machines 101. 
Apr 29, 2014 
LM101002: How to Build a Machine that Learns to Play Checkers
24:24
Learning Machines 101  A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this episode, we explain how to build a machine that learns to play checkers. The solution to this problem involves several key ideas which are fundamental to building systems which are artificially intelligent. Show Notes: Hello everyone! Welcome to the second podcast in the podcast series Learning Machines 101. In this series of podcasts my. Read More » The post LM101002: How to Build a Machine that Learns to Play Checkers appeared first on Learning Machines 101. 
Apr 29, 2014 