A nice little slide deck. It’s kind of like a one-deck overview of the content of (say) Udacity’s UD120 course, but without being afraid of using a little math. Worth a look for those getting into ML.
Online ML courses (and tutorials) are funny; they either eschew the use of equations and take an exposition-led approach that leads to surface “understanding” but no depth, or they take a math-heavy approach that frequently obscures the actual simplicity of many of the methods under a layer of equations that many people find impenetrable on first look. I’m yet to find the “Goldilocks” ML course (“just right”!) but if anyone has a suggestion, I’d be happy to hear it! :)
(FYI I’ve reviewed both Andrew Ng’s coursera course and Seb Thrun’s UD120, and I’ve read plenty of Murphy, Bishop, Hastie et al, and MacKay … Either too hot or too cold it seems!)
A nice little slide deck. It’s kind of like a one-deck overview of the content of (say) Udacity’s UD120 course, but without being afraid of using a little math. Worth a look for those getting into ML.
Online ML courses (and tutorials) are funny; they either eschew the use of equations and take an exposition-led approach that leads to surface “understanding” but no depth, or they take a math-heavy approach that frequently obscures the actual simplicity of many of the methods under a layer of equations that many people find impenetrable on first look. I’m yet to find the “Goldilocks” ML course (“just right”!) but if anyone has a suggestion, I’d be happy to hear it! :)
(FYI I’ve reviewed both Andrew Ng’s coursera course and Seb Thrun’s UD120, and I’ve read plenty of Murphy, Bishop, Hastie et al, and MacKay … Either too hot or too cold it seems!)