<p>This particular podcast covers the material in Chapter 2 of my new book <a href= "https://statisticalmachinelearning.com">“Statistical Machine Learning: A unified framework”</a> with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for Concept Modeling”.</p>

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 knowledge using set theory notation. Chapter 2 is titled “Set Theory for Concept Modeling”.

<p>This particular podcast covers the material in Chapter 2 of my new book <a href="https://statisticalmachinelearning.com" rel="noopener noreferrer">“Statistical Machine Learning: A unified framework”</a> with expected publication date May 2020. In this episode we discuss Chapter 2 of my new book, which discusses how to represent knowledge using set theory notation. Chapter 2 is titled “Set Theory for Concept Modeling”.</p>

LM101-080: Ch2: How to Represent Knowledge using Set Theory

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!

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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!

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LM101-080: Ch2: How to Represent Knowledge using Set Theory

LM101-080: Ch2: How to Represent Knowledge using Set Theory