Machine Learning Books and Lectures

Jordan, one of the readers of the blog, wrote to point out some cool references for machine learning, mathematics and artificial intelligence.

Thanks again for all your contributions. I’m a .NET programmer coming from a background of studying politics and Latin American studies so when the moment came that I realized I was interested in A.I., I didn’t have many resources to turn to.

I have a collection of books and links that I found very very helpful when I was trying to learn about these concepts. I was thinking they could be useful to some of your readers who were in my shoes two years ago. So without further adieu:

Handbook of Normal Frames and Coordinates

"Handbook of Normal Frames and Coordinates," Iliev, Bozhidar Z. (2006)

“This can be a heavy text, but if you understand the concept, this can take you to the next level – I would categorize this as Topology but has applications in quantum physics, theoretical mathematics, theoretical physics etc…- in context of your blogs – granted this is a dedicated read – I think readers will be able to appreciate and truly understand what a Hilbert Space is after reading this book.”

Linear Algebra

"Linear Algebra," Jim Heffron (2008)

“I liked this one because it was free and is still highly rated – I have also heard great reviews about David Poole’s book on linear algebra, but I couldn’t get myself to shell out the money when this was free.”

Complex To Real

“There are a ton of great articles here – I have personally read the ones on fourier transforms and wavelet transforms – great stuff”

Stanford Lectures – Fourier Analysis series

(free through Youtube or iTunesU)

“Email the Professor for the companion book. At this point it may have been published – but well worth shelling out the dough in any case.”

Bio-Inspired Artificial Intelligence

"Bio-Inspired Artificial Intelligence," Floreano and Mattiussi (2008)

“Excellent reference – fairly in depth for the number of topics it covers, lots of illustrations (illustrations are always a good thing 🙂 and I’ve also found it to be a useful source for inspiration. I bought this book while I was looking into fuzzy swarm intelligence – it doesn’t have all the answers, but I am simply in love with this book.”

Video lectures on Machine Learning

“A collection of video lectures featuring very brilliant people – let’s face it… if you’re doing or are interested in mathematics this complex… you probably don’t know too many people who you can talk to / learn from on the subject unless you’re in a University studying this kind of thing – these are some great lectures on machine learning – I just recently found this site but wish I had found it sooner – it’s great if you’re just exploring machine learning or are very well versed in it – however, I fall somewhere in the middle of that distribution so take it for what it’s worth!”

Fearless Symmetry

"Fearless Symmetry," Ash and Gross (2006)

Another accessible book to those without heavy training in math – great intro to Galois Theory, the Riemann Hypothesis and several other concepts.

Zero: The Biography of a Dangerous Idea

    "Zero: The Biography of a Dangerous Idea," Charles Seife (2000)

This one is more historical and conceptual than technical but it’s a captivating read and will help get you through those hard times when you want to put down that book on K-Dimensional Manifolds, but still need to satisfy your mathematical mind (some say it’s never at rest once you catch that "learning bug").

Khan Academy

Finally,  when you get lost, go here! The Khan Academy is a not-for-profit 501(c)(3) with the mission of providing a world-class education to anyone, anywhere.


Thanks Jordan! I hope readers can enjoy those resources as much as I did. Cheers!

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