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 define machine learning algorithms. Briefly, a learning machine is viewed as a dynamical system that is minimizing an objective function. In addition, the knowledge structure of the learning machine is interpreted as a preference relation graph which is implicitly specified by the objective function. In addition, this week we include in our book review section a new book titled “The Practioner’s Guide to Graph Data” by Denise Gosnell and Matthias Broecheler. To find out more information visit the website: www.learningmachines101.com .
LM101-006: How to Interpret Turing Test Results
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test)
LM101-004: Can computers think? A mathematician.s response
LM101-003: How to Represent Knowledge using Logical Rules
LM101-002: How to Build a Machine that Learns to Play Checkers
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