# Learning Machines 101

## By Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.

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#### 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!

Name | Description | Released | Price | ||
---|---|---|---|---|---|

1 |
CleanLM101-074: How to Represent Knowledge using Logical Rules (remix) | 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 know | 6/29/2018 | Free | View in iTunes |

2 |
CleanLM101-073: How to Build a Machine that Learns to Play Checkers (remix) | 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 c | 4/25/2018 | Free | View in iTunes |

3 |
CleanLM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002) | 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 | 3/30/2018 | Free | View in iTunes |

4 |
CleanLM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets | 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. Th | 2/23/2018 | Free | View in iTunes |

5 |
CleanLM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding | 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 i | 1/31/2018 | Free | View in iTunes |

6 |
CleanLM101-069: What Happened at the 2017 Neural Information Processing Systems Conference? | 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 examples. In | 12/16/2017 | Free | View in iTunes |

7 |
CleanLM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms | 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. T | 9/25/2017 | Free | View in iTunes |

8 |
CleanLM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun) | 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. S | 8/21/2017 | Free | View in iTunes |

9 |
CleanLM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun) | In this episode of Learning Machines 101 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 | 7/17/2017 | Free | View in iTunes |

10 |
CleanLM101-065: How to Design Gradient Descent Learning Machines (Rerun) | In this episode 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. | 6/19/2017 | Free | View in iTunes |

11 |
CleanLM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun) | In this episode 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 s | 5/15/2017 | Free | View in iTunes |

12 |
CleanLM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine | 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 reinf | 4/19/2017 | Free | View in iTunes |

13 |
CleanLM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine | This 62nd episode of Learning Machines 101 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 | 3/18/2017 | Free | View in iTunes |

14 |
CleanLM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN) | 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 Learn | 2/22/2017 | Free | View in iTunes |

15 |
CleanLM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms | 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 b | 1/22/2017 | Free | View in iTunes |

16 |
CleanLM101-059: How to Properly Introduce a Neural Network | 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 origin | 12/20/2016 | Free | View in iTunes |

17 |
CleanLM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis | 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 In | 11/22/2016 | Free | View in iTunes |

18 |
CleanLM101-057: How to Catch Spammers using Spectral Clustering | In this 57th episode, we explain how to use spectral cluster analysis unsupervised machine learning algorithms to catch internet criminals who try to steal your money electronically! | 10/17/2016 | Free | View in iTunes |

19 |
CleanLM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications | 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 | 9/19/2016 | Free | View in iTunes |

20 |
CleanLM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun) | 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 | 8/15/2016 | Free | View in iTunes |

21 |
CleanLM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN) | Learn how to use Latent Semantic Analysis for Document Retrieval and Market Basket Analysis | 7/25/2016 | Free | View in iTunes |

22 |
CleanLM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization) | 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 | 7/11/2016 | Free | View in iTunes |

23 |
CleanLM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear | 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 behind “The Kernel Trick” is that an impossible machine learning problem can be t | 6/13/2016 | Free | View in iTunes |

24 |
CleanLM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning[Rerun] | This particular podcast 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. | 5/24/2016 | Free | View in iTunes |

25 |
CleanLM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN] | In this episode we describe how to download and use free linear machine learning software to make predictions for classifying flower species using a famous machine learning data set. This is a RERUN of Episode 13. | 5/3/2016 | Free | View in iTunes |

26 |
CleanLM101-049: How to Experiment with Lunar Lander Software | 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 experime | 4/22/2016 | Free | View in iTunes |

27 |
CleanLM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun) | 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 lu | 3/28/2016 | Free | View in iTunes |

28 |
CleanLM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun) | 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 Vect | 3/14/2016 | Free | View in iTunes |

29 |
CleanLM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology) | 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 confer | 2/22/2016 | Free | View in iTunes |

30 |
CleanLM101-045: How to Build a Deep Learning Machine for Answering Questions about Images | 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 | 2/8/2016 | Free | View in iTunes |

31 |
CleanLM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference? | 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 Learn | 1/25/2016 | Free | View in iTunes |

32 |
CleanLM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22) | Welcome to the 43rd Episode of Learning Machines 101! We are currently presenting a subsequence of episodes covering the events of the recent Neural Information Processing Systems Conference. However, this week will digress with a rerun of Episode 22 wh | 1/11/2016 | Free | View in iTunes |

33 |
CleanLM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference? | 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 Lear | 12/28/2015 | Free | View in iTunes |

34 |
CleanLM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial? | 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 | 12/15/2015 | Free | View in iTunes |

35 |
CleanLM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis | In this episode we introduce a very powerful approach for computing semantic similarity between documents. Here, the terminology “document” could refer to a web-page, a word document, a paragraph of text, an essay, a sentence, or even just a single | 11/23/2015 | Free | View in iTunes |

36 |
CleanLM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun] | 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 | 11/9/2015 | Free | View in iTunes |

37 |
CleanLM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets | In this episode, we examine the problem of developing an advanced artificially intelligent technology which is capable of tracking knowledge growth in students in real-time, representing the knowledge state of a student a skill profile, and automaticall | 10/26/2015 | Free | View in iTunes |

38 |
CleanLM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory | In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). Such a machine could then use that information to optimize the choice and order of questions to be presented to the s | 10/12/2015 | Free | View in iTunes |

39 |
CleanLM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks | 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 | 9/28/2015 | Free | View in iTunes |

40 |
CleanLM101-035: What is a Neural Network and What is a Hot Dog? | 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. | 9/14/2015 | Free | View in iTunes |

41 |
CleanLM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun] | LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun] | 8/24/2015 | Free | View in iTunes |

42 |
CleanLM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN] | In this episode we describe how to download and use free linear machine learning software to make predictions for classifying flower species using a famous machine learning data set. | 8/10/2015 | Free | View in iTunes |

43 |
CleanLM101-032: How To Build a Support Vector Machine to Classify Patterns | Support vector parameter estimation and its relationship to logistic regression | 7/13/2015 | Free | View in iTunes |

44 |
CleanLM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN) | In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learning algorithms. | 6/21/2015 | Free | View in iTunes |

45 |
CleanLM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging) | 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. | 6/8/2015 | Free | View in iTunes |

46 |
CleanLM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling | 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 t | 5/25/2015 | Free | View in iTunes |

47 |
CleanLM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN] | 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 | 5/11/2015 | Free | View in iTunes |

48 |
CleanLM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN] | In this podcast episode, we discuss the design of statistical learning machines which can make inferences about rare and unseen events using prior knowledge. | 4/27/2015 | Free | View in iTunes |

49 |
CleanLM101-026: How to Learn Statistical Regularities (Rerun) | 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. | 4/13/2015 | Free | View in iTunes |

50 |
CleanLM101-025: How to Build a Lunar Lander Autopilot Learning Machine | 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 lu | 3/23/2015 | Free | View in iTunes |

51 |
CleanLM101-024: How to Use Genetic Algorithms to Breed Learning Machines | In this episode 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. | 3/9/2015 | Free | View in iTunes |

52 |
CleanLM101-023: How to Build a Deep Learning Machine | In this episode we discuss how to design and build “Deep Learning Machines” which can autonomously discover useful ways to represent knowledge of the world. | 2/23/2015 | Free | View in iTunes |

53 |
CleanLM101-022: How to Learn to Solve Large Constraint Satisfaction Problems | In this episode we discuss how to learn to solve constraint satisfaction inference problems. | 2/9/2015 | Free | View in iTunes |

54 |
CleanLM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain) | 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 | 1/26/2015 | Free | View in iTunes |

55 |
CleanLM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions | 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. | 1/12/2015 | Free | View in iTunes |

56 |
CleanLM101-019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition) | 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. | 12/21/2014 | Free | View in iTunes |

57 |
CleanLM101-018: Can Computers Think? A Mathematician's Response (Rerun) | 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 pertai | 12/12/2014 | Free | View in iTunes |

58 |
CleanLM101-017: How to Decide if a Machine is Artificially Intelligent (Rerun) | This is rerun of an Episode which includes an interview with the chatbot ALICE and how you can build your own chatbot! | 11/24/2014 | Free | View in iTunes |

59 |
CleanLM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods | In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learning algorithms. | 11/10/2014 | Free | View in iTunes |

60 |
CleanLM101-015: How to Build a Machine that Can Learn Anything (The Perceptron) | In episode 15 of Learning Machines 101 we describe 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! | 10/27/2014 | Free | View in iTunes |

61 |
CleanLM101-014: How to Build a Machine that Can Do Anything (Function Approximation) | 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 tha | 10/13/2014 | Free | View in iTunes |

62 |
CleanLM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software) | In this episode we describe how to download and use free linear machine learning software to make predictions for classifying flower species using a famous machine learning data set. | 9/22/2014 | Free | View in iTunes |

63 |
CleanLM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods) | 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. | 9/8/2014 | Free | View in iTunes |

64 |
CleanLM101-008: How to Represent Beliefs Using Probability Theory | 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 in | 9/3/2014 | Free | View in iTunes |

65 |
CleanLM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws) | 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: | 8/25/2014 | Free | View in iTunes |

66 |
CleanLM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation) | 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. | 8/11/2014 | Free | View in iTunes |

67 |
CleanLM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition) | 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 inte.. | 7/28/2014 | Free | View in iTunes |

68 |
CleanLM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory | 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 d.. | 6/23/2014 | Free | View in iTunes |

69 |
CleanLM101-006: How to Interpret Turing Test Results | 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, | 6/9/2014 | Free | View in iTunes |

70 |
CleanLM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test) | 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. | 5/26/2014 | Free | View in iTunes |

71 |
CleanLM101-004: Can computers think? A mathematician.s response | 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 s.. | 5/12/2014 | Free | View in iTunes |

72 |
CleanLM101-003: How to Represent Knowledge using Logical Rules | 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. | 4/28/2014 | Free | View in iTunes |

73 |
CleanLM101-002: How to Build a Machine that Learns to Play Checkers | 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: | 4/28/2014 | Free | View in iTunes |

73 Items |

#### Customer Reviews

##### Cool Podcast

Hi there, this is so much fun to listen to, I have been listening to all of your podcasts, and like how each episode teaches me something new, information that is easy to understand because of your analogies, it is very “user friendly”. I have told a lot of people about your podcast, I hope they have been listening! This is by far the best discussion on Machine Learning that I have heard. You should stay in the number 1 spot, you have my vote ! I will be back !! Love that part too!

##### Great content in here

Long Intro, but the General Content is really good. Pretty simple to follow and to understand this complicated concepts

##### Excellent

Love it, can you talk about tools in machine learning for data science? Thanks!

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- Category: Software How-To
- Language: English
- © Copyright (c) 2014-2017 by Richard M. Golden. All rights reserved.

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