LM101-004: Can computers think? A mathematician.s response

LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes: This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of observed outcomes in a space-time continuum which corresponds to our physical world. The machine learning algorithm uses information by Learning Machines 101

35 min listen

LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges: This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed to minimize smooth non-convex objective functions using batch learning methods. In particular, a broad class... by Learning Machines 101

31 min listen

LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems: In this episode of Learning Machines 101, we review Chapter 6 of my book “Statistical Machine Learning” which introduces methods for analyzing the behavior of machine inference algorithms and machine learning algorithms as dynamical systems. We show t by Learning Machines 101

33 min listen

LM101-083: Ch5: How to Use Calculus to Design Learning Machines: This particular podcast covers the material from Chapter 5 of my new book “Statistical Machine Learning: A unified framework” which is now available! The book chapter shows how matrix calculus is very useful for the analysis and design of both linear by Learning Machines 101

34 min listen

LM101-082: Ch4: How to Analyze and Design Linear Machines: The main focus of this particular episode covers the material in Chapter 4 of my new forthcoming book titled “Statistical Machine Learning: A unified framework.” Chapter 4 is titled “Linear Algebra for Machine Learning by Learning Machines 101

29 min listen

LM101-081: Ch3: How to Define Machine Learning (or at Least Try): This particular podcast covers the material in Chapter 3 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 3 of my new book which discusses how to formally de by Learning Machines 101

37 min listen

LM101-080: Ch2: How to Represent Knowledge using Set Theory: This particular podcast covers the material in Chapter 2 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent by Learning Machines 101

32 min listen

LM101-079: Ch1: How to View Learning as Risk Minimization: This particular podcast covers the material in Chapter 1 of my new (unpublished) book “Statistical Machine Learning: A unified framework”. In this episode we discuss Chapter 1 of my new book, which shows how supervised, unsupervised, and reinforcement by Learning Machines 101

26 min listen

LM101-078: Ch0: How to Become a Machine Learning Expert: This particular podcast is the initial episode in a new special series of episodes designed to provide commentary on a new book that I am in the process of writing. In this episode we discuss books, software, courses, and podcasts designed to help you bec by Learning Machines 101

39 min listen

LM101-077: How to Choose the Best Model using BIC: In this episode, we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. Briefly, by Learning Machines 101

24 min listen

LM101-076: How to Choose the Best Model using AIC and GAIC: How to Select the Learning Machine with the Best Generalization Performance using Only Training Data by Learning Machines 101

28 min listen

LM101-075: Can computers think? A Mathematician's Response (remix): 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 by Learning Machines 101

36 min listen

LM101-074: 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 knowle by Learning Machines 101

19 min listen

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 cla by Learning Machines 101

25 min listen

LM101-072: 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 f by Learning Machines 101

22 min listen

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. Thir by Learning Machines 101

32 min listen

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 inc by Learning Machines 101

32 min listen

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 ad by Learning Machines 101

23 min listen

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. Thi by Learning Machines 101

22 min listen

LM101-067: 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. Spe by Learning Machines 101

26 min listen

LM101-066: 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 o by Learning Machines 101

34 min listen

LM101-065: 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. by Learning Machines 101

30 min listen

LM101-064: 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 sea by Learning Machines 101

28 min listen

LM101-063: 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 reinfor by Learning Machines 101

22 min listen

LM101-062: 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 t by Learning Machines 101

31 min listen

LM101-061: 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 Learnin by Learning Machines 101

29 min listen

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 bas by Learning Machines 101

30 min listen

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. by Learning Machines 101

30 min listen

LM101-058: 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 Info by Learning Machines 101

20 min listen

LM101-057: 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! by Learning Machines 101

20 min listen

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LM101-004: Can computers think? A mathematician.s response

LM101-004: Can computers think? A mathematician.s response

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