Episode Summary: 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 and nonlinear learning machines with lots of examples. Show Notes: Hello everyone! Welcome to the… Read More »
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Episode Summary: This particular podcast covers the material in Chapter 4 of my new book “Statistical Machine Learning: A unified framework” which is now available! Many important and widely used machine learning algorithms may be interpreted as linear machines and this chapter shows how to use linear algebra to analyze and design such machines. In addition, these same… Read More »
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A large class of complex machine learning algorithms can be represented as dynamical systems which are minimizing an objective function with respect to a preference relation.
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Episode Summary: 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”. Show… Read More »
