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Make sure to visit: www.learningmachines101.com

to obtain free transcripts of this podcast and important

supplemental reference materials!

Make sure to visit: www.learningmachines101.com to obtain free transcripts of this podcast and important supplemental reference materials!

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to obtain a transcript of this episode!

]]>to obtain a transcript of this episode!

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This episode is a RERUN of an episode originally presented in January 2016 and lays the groundwork for future episodes on the topic of reinforcement learning!

Check out: www.learningmachines101.com for more info!!

]]>This episode is a RERUN of an episode originally presented in January 2016 and lays the groundwork for future episodes on the topic of reinforcement learning!

Check out: www.learningmachines101.com for more info!!

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and follow us on twitter at: @lm101talk

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and follow us on twitter at: @lm101talk

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Check it out at: www.learningmachines101.com

and follow us on twitter: @lm101talk

]]>Check it out at: www.learningmachines101.com

and follow us on twitter: @lm101talk

]]>and also check us out on twitter (@lm101talk).

]]>and also check us out on twitter (@lm101talk).

]]>where you can obtain transcripts of this episode and download free machine learning software! Also check out the "Statistical Machine Learning Forum" on Linked In and follow us on Twitter using the twitter handle: @lm101talk

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where you can obtain transcripts of this episode and download free machine learning software! Also check out the "Statistical Machine Learning Forum" on Linked In and follow us on Twitter using the twitter handle: @lm101talk

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It's important to actually experiment with real machine learning software if you want to learn about machine learning!

Check out: www.learningmachines101.com

to obtain transcripts of this podcast and download free machine learning software!

Or tweet us at: @lm101talk

]]>

It's important to actually experiment with real machine learning software if you want to learn about machine learning!

Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

Or tweet us at: @lm101talk

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twitter at: lm101talk

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to download the free lunar lander software which illustrates principles of temporal reinforcement learning and nonlinear control theory. You will also have the opportunity to download free software which illustrates how a simple deep learning neural network with one layer of radial basis functions works and a simple linear regression model learning machine. Check it out!!!

]]>to download the free lunar lander software which illustrates principles of temporal reinforcement learning and nonlinear control theory. You will also have the opportunity to download free software which illustrates how a simple deep learning neural network with one layer of radial basis functions works and a simple linear regression model learning machine. Check it out!!!

]]>and visit us a twitter: @lm101talk #machinelearning #statistics

#artificialintelligence

]]>and visit us a twitter: @lm101talk #machinelearning #statistics

#artificialintelligence

]]>For more information..check us out at: www.learningmachines101.com

also check us out on twitter at: lm101talk

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also check us out on twitter at: lm101talk

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www.learningmachines101.com and follow us on Twitter at: @lm101talk

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www.learningmachines101.com and follow us on Twitter at: @lm101talk

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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 which nicely complements our previous discussion of the Monte Carlo Markov Chain Algorithm Tutorial. Specifically, today we

discuss the problem of approaches for learning or equivalently parameter estimation in Monte Carlo Markov Chain algorithms. The topics covered in this episode include: What is the pseudolikelihood method and what are its advantages and disadvantages?

What is Monte Carlo Expectation Maximization? And...as a bonus prize...a mathematical theory of "dreaming"!!! The current plan is to return

to coverage of the Neural Information Processing Systems Conference in 2 weeks on January 25!! Check out: www.learningmachines101.com for more details!]]>

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for additional supplementary hyperlinks to the conference and conference papers!!]]>

Also feel free to visit us at twitter: @lm101talk

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Also feel free to visit us at twitter: @lm101talk

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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!!

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See you soon!!

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"How to Use Nonlinear Machine Learning Software to Make Predictions".

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 a feedforward artificial neural network whose hidden units are radial basis functions. This is essentially a nonlinear regression modeling problem. Check out: www.learningmachines101.com

and follow us on twitter: @lm101talk

"How to Use Nonlinear Machine Learning Software to Make Predictions".

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 a feedforward artificial neural network whose hidden units are radial basis functions. This is essentially a nonlinear regression modeling problem. Check out: www.learningmachines101.comand follow us on twitter: @lm101talk

]]>In this episode will explain how to download and use free machine learning software which can be downloaded from the website: www.learningmachines101.com. The software can be used to make predictions using your own data sets. 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.This is a rerun of Episode 13.

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Check out: www.learningmachines101.com to obtain transcripts of this podcast

and access to free machine learning software!

Check out: www.learningmachines101.com to obtain transcripts of this podcastand access to free machine learning software!

]]>

Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

]]>

]]>

to obtain transcripts of this podcast and download free machine learning software!

]]>to obtain transcripts of this podcast and download free machine learning software!

]]>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!

]]>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!

]]>Please visit: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

]]>Please visit: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

]]>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!!!

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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!!!

]]>

For more podcast episodes on the topic of machine learning and free machine learning software, please visit us at: www.learningmachines101.com !!

]]>For more podcast episodes on the topic of machine learning and free machine learning software, please visit us at: www.learningmachines101.com !!

]]>Check out: www.learningmachines101.com

to obtain transcripts of this podcast!!!

Check out: www.learningmachines101.com to obtain transcripts of this podcast!!!

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

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 to make virtually any kind of prediction you like. However, some of these predictions will be good predictions, while other predictions will be poor predictions. For this reason, following the discussion in Episode 12 which was concerned with the problem of evaluating generalization performance, we will also discuss how to evaluate what your learning machine has “memorized” and additionally evaluate the ability of your learning machine to “generalize” and make predictions about things that it has never seen before.

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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 to make virtually any kind of prediction you like. However, some of these predictions will be good predictions, while other predictions will be poor predictions. For this reason, following the discussion in Episode 12 which was concerned with the problem of evaluating generalization performance, we will also discuss how to evaluate what your learning machine has “memorized” and additionally evaluate the ability of your learning machine to “generalize” and make predictions about things that it has never seen before.

]]>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.

]]>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.

]]>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.

]]>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.

]]>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.

]]>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.

]]>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.

]]>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.

]]>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, then the computer should be called .artificially intelligent.. Some objections to this definition of artificial intelligence are introduced and discussed. At. Read More »

The post LM101-006: How to Interpret Turing Test Results appeared first on Learning Machines 101.

]]>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, then the computer should be called .artificially intelligent.. Some objections to this definition of artificial intelligence are introduced and discussed. At. Read More »

The post LM101-006: How to Interpret Turing Test Results appeared first on Learning Machines 101.

]]>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.

]]>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.

]]>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.

]]>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.

]]>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. The challenges of representing knowledge using rules are also discussed. Specifically, these challenges include: issues of feature representation, having an. Read More »

The post LM101-003: How to Represent Knowledge using Logical Rules appeared first on Learning Machines 101.

]]>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. The challenges of representing knowledge using rules are also discussed. Specifically, these challenges include: issues of feature representation, having an. Read More »

The post LM101-003: How to Represent Knowledge using Logical Rules appeared first on Learning Machines 101.

]]>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: Hello everyone! Welcome to the second podcast in the podcast series Learning Machines 101. In this series of podcasts my. Read More »

The post LM101-002: How to Build a Machine that Learns to Play Checkers appeared first on Learning Machines 101.

]]>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: Hello everyone! Welcome to the second podcast in the podcast series Learning Machines 101. In this series of podcasts my. Read More »

The post LM101-002: How to Build a Machine that Learns to Play Checkers appeared first on Learning Machines 101.

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