Rank #1: LM101-035: 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. Check out: www.learningmachines101.com for videos of a neural network that learns to play ATARI video games and transcripts of this podcast!!! Also follow us on twitter at: @lm101talk
See you soon!!
Sep 15 2015
Rank #2: LM101-059: 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 origins. For more details visit us at: www.learningmachines101.com
Dec 21 2016
Rank #3: LM101-004: 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 such limits pertain to biological brains and other non-standard computing machines. Show Notes: Hello everyone! Welcome to the. Read More »
The post LM101-004: Can computers think? A mathematician.s response appeared first on Learning Machines 101.
May 12 2014
Rank #4: LM101-070: 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 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 K-means 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 to obtain free transcripts of this podcast and important supplemental reference materials!
Jan 31 2018
Rank #5: LM101-068: 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. 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: www.learningmachines101.com
Sep 26 2017
Rank #6: LM101-023: How to Build a Deep Learning Machine
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
Rank #7: LM101-047: 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 Vector Machine decision boundary. The relationship of Support Vector Machine parameter estimation and logistic regression parameter estimation is also discussed.For more information..check us out at: www.learningmachines101.com
also check us out on twitter at: lm101talk
Mar 14 2016
Rank #8: LM101-069: 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 addition, a book review of the book “Deep Learning” is provided. #nips2017
Dec 16 2017
Rank #9: LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
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
Rank #10: LM101-032: How To Build a Support Vector Machine to Classify Patterns
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 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
Rank #11: LM101-005: 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. (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 LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test) appeared first on Learning Machines 101.
May 27 2014
Rank #12: LM101-045: 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. Check out: www.learningmachines101.com for additional details!!
Feb 08 2016
Rank #13: LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
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
Rank #14: LM101-021: 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 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
Rank #15: LM101-060: 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 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
Rank #16: LM101-010: 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. 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 LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation) appeared first on Learning Machines 101.
Aug 12 2014
Rank #17: LM101-071: 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. Third, we describe a large database of common sense knowledge where the knowledge is represented using first-order logic which is free for researchers in machine learning. We provide a hyperlink to this free database of common sense knowledge. Fourth, we discuss some problems of first-order 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
Rank #18: LM101-040: 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 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
Rank #19: LM101-073: 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 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
Rank #20: LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
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 self-driving car!!
Mar 19 2017