Gentle introductions to AI, ML, and DI

Recently, a colleague asked me for some layman introductions to AI and machine learning.  He’s on the road and in a bit of a hurry, so I put together a few online resources.  A neat trick: read free introductory Kindle chapters at Amazon, or download samples to your device. Here’s what I recommended:

  • AI: Artificial Intelligence: A Modern Approach (read starting with the introduction here). I think of Norvig as a father of the field.
  • Machine learning: Machine Learning by Peter Flash (read the prologue that you can find here).
  • Transfer: Learning to Learn, by me and Sebastian Thrun (read the two pages on transfer here)
  • Deep Learning: The next generation of neural networks a great talk by Geoff Hinton at Google.  Also see this online book (by Bengio, Goodfellow, and Courville) for an up-to-date review of where we’re at today, including how these multiple topics relate.
  • Decision Intelligence: If you go to this blog’s front page and click “Sign up now!”, you’ll receive a free copy of my ebook: Decision Intelligence: A Primer.  This post explains how machine learning connects to Decision Intelligence.  This video of my presentation at CMU describes how DI evolved from ML. And here is a video series about DI that I put together.

If you choose to dive more deeply, and build a learner reasonably painlessly yourself, I do like Machine Learning with R, which is a practical, step-by-step approach to using the open-source R package (easy to download and install to PCs or Macs).  A similarly accessible book is Machine Learning for Hackers. It also uses R, with a focus on text processing.

[bctt tweet=”R is amazing, and hands-down the easiest way to get started in machine learning.”]

Do you have any favorite resources?  Please suggest them in the comments, thank you!

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