Learning Machines 101

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 podcastand access to free machine learning software!

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!  

In this episode we introduce the concept of learning machines that can self-evolve using simulated natural evolution into more intelligent machines using Monte Carlo Markov Chain Genetic Algorithms. Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

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 ! 

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! 

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!

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!

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!

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. 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!!!  

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.

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 !!

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 to obtain transcripts of this podcast!!!

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 for a transcript of this show and free machine learning software!

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

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. 

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.

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 LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws) appeared first on Learning Machines 101.

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 LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation) appeared first on Learning Machines 101.

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 LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition) appeared first on Learning Machines 101.

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 LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory appeared first on Learning Machines 101.