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 book chapter (by Bengio, Goodfellow, and Courville) for an up-to-date (still in draft) 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.
Do you have any favorite resources? Please suggest them in the comments, thank you!