Machine Learning Crash Course: Part 2

I may be off base here but wouldn't CV be part of what you're interested in? If so I've found Adrian Rosebrock has some great paid for and free options:

https://www.pyimagesearch.com (free tutorials in the blog) https://www.pyimagesearch.com/pyimagesearch-gurus/ (paid for "guru" course)

There's a really straightforward approach to turning video into feature vectors which you can readily plug into any old ML algorithm. You can turn every decoded frame (which is essentially a PNG, right?) into a (Width*Height)x1 vector, where each cell is an RGB pixel. You can then compose these vectors into a matrix, or perform further operations on them, e.g. SVD. Whether or not this is a good approach, though, will depend of course on your application. For face detection, for example, this is not adequate, and you'll need more sophisticated algorithms. For a good overview of that, see Jason Lawrence's slides[1] and also take a look at some of the other stuff he covered in his Computer Vision course[2].

Sorry if that's too basic for what you were asking. If you want to see some messy code that does this using OpenCV, here's some I wrote a while back with a friend, starting on line 127: https://github.com/sprestwood/CompVisionS2015/blob/master/te...

[1] http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/le...

[2] http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/sc...

I'll tell you how I started my journey:

I took the Stanford ML Class in 2011 taught by Andrew Ng; ultimately, Coursera was born from it, and you can still find that class in their offerings:

https://www.coursera.org/learn/machine-learning

On a similar note, Udacity sprung up from the AI Class that ran at the same time (taught by Peter Norvig and Sebastian Thrun); Udacity has since added the class to their lineup (though at the time, they had trouble doing this - and so spawned the CS373 course):

https://www.udacity.com/course/intro-to-artificial-intellige...

https://www.udacity.com/course/artificial-intelligence-for-r...

I took the CS373 course later in 2012 (I had started the AI Class, but had to drop out due to personal issues at the time).

Today I am currently taking Udacity's "Self-Driving Car Engineer" nanodegree program.

But it all started with the ML Class. Prior to that, I had played around with things on my own, but nothing really made a whole lot of sense for me, because I lacked some of the basic insights, which the ML Class course gave to me.

Primarily - and these are key (and if you don't have an idea about them, then you should study them first):

1. Machine learning uses a lot of tools based on and around probabilities and statistics.

2. Machine learning uses a good amount of linear algebra

3. Neural networks use a lot of matrix math (which is why they can be fast and scale - especially with GPUs and other multi-core systems)

4. If you want to go beyond the "black box" aspect of machine learning - brush up on your calculus (mainly derivatives).

That last one is what I am currently struggling with and working through; while the course I am taking currently isn't stressing this part, I want to know more about what is going on "under the hood" so to speak. Right now, we are neck deep into learning TensorFlow (with Python); TensorFlow actually makes things pretty simple to create neural networks, but having the understanding of how forward and back-prop works (because in the ML Class we had to implement this using Octave - we didn't use a library) has been extremely helpful.

Did I find the ML Class difficult? Yeah - I did. I hadn't touched linear algebra in 20+ years when I took the course, and I certainly hadn't any skills in probabilities (so, Kahn Academy and the like to the rescue). Even now, while things are a bit easier, I am still finding certain tasks and such challenging in this nanodegree course. But then, if you aren't challenged, you aren't learning.

Thanks for pointers! I'm basically below/at 101 with ML, but have background in computer graphics.

I was wondering if one could utilise ML for either or both of two things: object outline spatial/temporal (feathering can solve for motion blur) and better Chroma key.

A comment I made within this post: Fitting a formula to data implies that there are reasons for the formula to be this and not something else. For example one can prove, within the current model, that the speed of a falling object is v(t) = 1/2 g t^2 and it is reasonable to fit experimental data to this formula to determine g. It may be a false assumption (it is, actually) and this is the whole idea of modelling and understanding limitations of a model.

In contrast, the article assumes that a linear expression will be fitted to any kind of data as if they behaved, by miracle, in a linear fashion. Any kind of deduction from this will be false, except if, again by miracle, the data actually behaves linearly.

I am a big fan of clusterisation and data behaviour discovery - the process which highlights relationship between data we do not know anything about. I believe this is a huge win in ML. Fitting something (1D, 2D, ...) to data without a model and drawing conclusions is at least perilous.