Learning Machines 101
Activity Overview
Episode publication activity over the past year
Episodes
LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
20 Jul 2021
Contributed by Lukas
This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of outcomes in a space-time con...
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
21 May 2021
Contributed by Lukas
This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed to minimi...
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
05 Jan 2021
Contributed by Lukas
In this episode of Learning Machines 101, we review Chapter 6 of my book "Statistical Machine Learning" which introduces methods for analyzing the beh...
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
29 Aug 2020
Contributed by Lukas
This particular podcast covers the material from Chapter 5 of my new book "Statistical Machine Learning: A unified framework" which is now available! ...
LM101-082: Ch4: How to Analyze and Design Linear Machines
23 Jul 2020
Contributed by Lukas
The main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled "Statistical Machine Learning: A unified ...
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
09 Apr 2020
Contributed by Lukas
This particular podcast covers the material in Chapter 3 of my new book "Statistical Machine Learning: A unified framework" with expected publication ...
LM101-080: Ch2: How to Represent Knowledge using Set Theory
29 Feb 2020
Contributed by Lukas
This particular podcast covers the material in Chapter 2 of my new book "Statistical Machine Learning: A unified framework" with expected publication ...
LM101-079: Ch1: How to View Learning as Risk Minimization
24 Dec 2019
Contributed by Lukas
This particular podcast covers the material in Chapter 1 of my new (unpublished) book "Statistical Machine Learning: A unified framework". In this epi...
LM101-078: Ch0: How to Become a Machine Learning Expert
24 Oct 2019
Contributed by Lukas
This particular podcast (Episode 78 of Learning Machines 101) is the initial episode in a new special series of episodes designed to provide commentar...
LM101-077: How to Choose the Best Model using BIC
02 May 2019
Contributed by Lukas
In this 77th episode of www.learningmachines101.com , we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and em...
LM101-076: How to Choose the Best Model using AIC and GAIC
23 Jan 2019
Contributed by Lukas
In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Information Criter...
LM101-075: Can computers think? A Mathematician's Response (remix)
12 Dec 2018
Contributed by Lukas
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, ...
LM101-074: How to Represent Knowledge using Logical Rules (remix)
30 Jun 2018
Contributed by Lukas
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 impl...
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
25 Apr 2018
Contributed by Lukas
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 Samu...
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
31 Mar 2018
Contributed by Lukas
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 Le...
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
23 Feb 2018
Contributed by Lukas
In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense into learning...
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
31 Jan 2018
Contributed by Lukas
This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique call...
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
16 Dec 2017
Contributed by Lukas
This 69th episode of Learning Machines 101 provides a short overview of the 2017 Neural Information Processing Systems conference with a focus on the ...
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
26 Sep 2017
Contributed by Lukas
This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithms which it...
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
21 Aug 2017
Contributed by Lukas
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 ...
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
17 Jul 2017
Contributed by Lukas
In this episode of Learning Machines 101 (www.learningmachines101.com) we discuss how to solve constraint satisfaction inference problems where knowle...
LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
19 Jun 2017
Contributed by Lukas
In this episode rerun we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of deep lear...
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
15 May 2017
Contributed by Lukas
In this rerun of episode 24 we explore the concept of evolutionary learning machines. That is, learning machines that reproduce themselves in the hope...
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
20 Apr 2017
Contributed by Lukas
This 63rd episode of Learning Machines 101 discusses how to build reinforcement learning machines which become smarter with experience but do not use ...
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
19 Mar 2017
Contributed by Lukas
This 62nd episode of Learning Machines 101 (www.learningmachines101.com) discusses how to design reinforcement learning machines using your knowledg...
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
23 Feb 2017
Contributed by Lukas
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 Inform...
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
23 Jan 2017
Contributed by Lukas
This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to monitor the...
LM101-059: How to Properly Introduce a Neural Network
21 Dec 2016
Contributed by Lukas
I discuss the concept of a "neural network" by providing some examples of recent successes in neural network machine learning algorithms and providing...
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
23 Nov 2016
Contributed by Lukas
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...
LM101-057: How to Catch Spammers using Spectral Clustering
18 Oct 2016
Contributed by Lukas
In this 57th episode, we explain how to use unsupervised machine learning algorithms to catch internet criminals who try to steal your money electroni...
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
20 Sep 2016
Contributed by Lukas
In this NEW episode we discuss Latent Semantic Indexing type machine learning algorithms which have a PROBABILISTIC interpretation. We explain why ...
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
16 Aug 2016
Contributed by Lukas
In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that ...
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
25 Jul 2016
Contributed by Lukas
Welcome to the 54th Episode of Learning Machines 101 titled "How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Lat...
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
11 Jul 2016
Contributed by Lukas
In this 53rd episode of Learning Machines 101, we introduce the concept of a Swarm Intelligence with respect to Particle Swarm Optimization Algorithms...
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
13 Jun 2016
Contributed by Lukas
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". T...
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun]
24 May 2016
Contributed by Lukas
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 ...
LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
04 May 2016
Contributed by Lukas
In this episode we will explain how to download and use free machine learning software from the website: www.learningmachines101.com. This podcast is ...
LM101-049: How to Experiment with Lunar Lander Software
22 Apr 2016
Contributed by Lukas
In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learning machine’...
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
29 Mar 2016
Contributed by Lukas
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...
LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)
14 Mar 2016
Contributed by Lukas
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 speci...
LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
23 Feb 2016
Contributed by Lukas
In this episode, we briefly review Item Response Theory and Bayesian Network Theory methods for the assessment and optimization of student learning an...
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
08 Feb 2016
Contributed by Lukas
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 ...
LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
26 Jan 2016
Contributed by Lukas
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 Info...
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
12 Jan 2016
Contributed by Lukas
Welcome to the 43rd Episode of Learning Machines 101!We are currently presenting a subsequence of episodes covering the events of the recent Neural I...
LM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
29 Dec 2015
Contributed by Lukas
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 Inf...
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
16 Dec 2015
Contributed by Lukas
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...
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
24 Nov 2015
Contributed by Lukas
In this episode we introduce a very powerful approach for computing semantic similarity between documents. Here, the terminology “document” coul...
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
09 Nov 2015
Contributed by Lukas
In this episode we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of c...
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets
27 Oct 2015
Contributed by Lukas
In this episode, we examine the problem of developing an advanced artificially intelligent technology which is capable of tracking knowledge growth in...
LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory
12 Oct 2015
Contributed by Lukas
In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). Suppose that y...
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
28 Sep 2015
Contributed by Lukas
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 Net...
LM101-035: What is a Neural Network and What is a Hot Dog?
15 Sep 2015
Contributed by Lukas
In this episode, we address the important questions of “What is a neural network?” and “What is a hot dog?” by discussing human brains, neur...
LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
25 Aug 2015
Contributed by Lukas
Welcome to the 34th podcast in the podcast series Learning Machines 101 titled "How to Use Nonlinear Machine Learning Software to Make Predictions". ...
LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
10 Aug 2015
Contributed by Lukas
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LM101-032: How To Build a Support Vector Machine to Classify Patterns
13 Jul 2015
Contributed by Lukas
In this 32nd episode of Learning Machines 101, we introduce the concept of a Support Vector Machine. We explain how to estimate the parameters of such...
LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)
21 Jun 2015
Contributed by Lukas
In this rerun of Episode 16, we introduce the important concept of gradient descent which is the fundamental principle underlying learning mechanisms ...
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
08 Jun 2015
Contributed by Lukas
Deep learning machine technology has rapidly developed over the past five years due in part to a variety of actors such as: better technology, convolu...
LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling
25 May 2015
Contributed by Lukas
This podcast discusses talks, papers, and ideas presented at the recent International Conference on Learning Representations 2015 which was followed b...
LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]
11 May 2015
Contributed by Lukas
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 generaliz...
LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]
28 Apr 2015
Contributed by Lukas
In this episode of Learning Machines 101 we discuss the design of statistical learning machines which can make inferences about rare and unseen event...
LM101-026: How to Learn Statistical Regularities (Rerun)
14 Apr 2015
Contributed by Lukas
In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that ...
LM101-025: How to Build a Lunar Lander Autopilot Learning Machine
24 Mar 2015
Contributed by Lukas
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...
LM101-024: How to Use Genetic Algorithms to Breed Learning Machines
10 Mar 2015
Contributed by Lukas
In this episode we introduce the concept of learning machines that can self-evolve using simulated natural evolution into more intelligent machines ...
LM101-023: How to Build a Deep Learning Machine
24 Feb 2015
Contributed by Lukas
Recently, there has been a lot of discussion and controversy over the currently hot topic of “deep learning”!! Deep Learning technology has made ...
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems
10 Feb 2015
Contributed by Lukas
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 pr...
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
26 Jan 2015
Contributed by Lukas
We discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated proba...
LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions
12 Jan 2015
Contributed by Lukas
In this episode we introduce some advanced nonlinear machine software which is more complex and powerful than the linear machine software introduced i...
LM101-019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
22 Dec 2014
Contributed by Lukas
Embodied cognition emphasizes the design of complex artificially intelligent systems may be both vastly simplified and vastly enhanced if we view the ...
LM101-018: Can Computers Think? A Mathematician's Response (Rerun)
12 Dec 2014
Contributed by Lukas
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...
LM101-017: How to Decide if a Machine is Artificially Intelligent (Rerun)
24 Nov 2014
Contributed by Lukas
This episode we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable ...
LM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods
11 Nov 2014
Contributed by Lukas
In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learnin...
LM101-015: How to Build a Machine that Can Learn Anything (The Perceptron)
27 Oct 2014
Contributed by Lukas
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 generat...
LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)
13 Oct 2014
Contributed by Lukas
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 mac...
LM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)
22 Sep 2014
Contributed by Lukas
Hello everyone! Welcome to the thirteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss import...
LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)
09 Sep 2014
Contributed by Lukas
In this episode we discuss the problem of how to evaluate the ability of a learning machine to make generalizations and construct abstractions given t...
LM101-008: How to Represent Beliefs Using Probability Theory
03 Sep 2014
Contributed by Lukas
Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probabil...
LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)
26 Aug 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: Today we address a strange yet fundamen...
LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)
12 Aug 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this podcast episode, we discuss fun...
LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
28 Jul 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: Embodied cognition emphasizes the desig...
LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory
23 Jun 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In real life, there is no certainty. Th...
LM101-006: How to Interpret Turing Test Results
09 Jun 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this episode, we briefly review the ...
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)
27 May 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: This episode we discuss the Turing Test...
LM101-004: Can computers think? A mathematician.s response
12 May 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this episode, we explore the questio...
LM101-003: How to Represent Knowledge using Logical Rules
29 Apr 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this episode we will learn how to us...
LM101-002: How to Build a Machine that Learns to Play Checkers
29 Apr 2014
Contributed by Lukas
Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning Episode Summary: In this episode, we explain how to buil...