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 it 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. And this video of my presentation at CMU describes how DI evolved from ML.
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.
Do you have any favorite resources? Please suggest them in the comments, thank you!