This week I started CS498 Applied Machine Learning at the University of Illinois. Getting suplexed by CS498 Database Systems last semester wasn't enough, I guess. There are still a few ribs left intact—let's break those, too.
Jokes aside, the reason I'm taking classes again is that I underestimated how I close I was to obsolescence at work—how close we all are, really. (Frederick Herzberg, Work and the Nature of Man: "How comfortable it is to be able to earn a living today on yesterday’s knowledge, but how often this leads to obsolescence.") You're either getting better or you're getting worse—you're sure as hell not staying in the same place. I used to think that was hokey, but now I believe it.
Back to machine learning: what the hell is it? I don't know. The professor just jumped right into classification of data—an indication of how fast the course will go, I'm sure. Implicitly, that means that if I want to know what machine learning is, its history, where it came from and how it got here, then I'm going to have to piece that together. So be it.
I'm going to use the same approach for this that I've used successfully for other topics where I'm a horrible dilettante: plow ahead, be humble and don't forget the difference between what I actually know and what I think know, and leave a trail for others who are starting from the bottom. I'm sure it's been documented a million times how much more a person can understand a topic when they have to teach it.
Here are some bits of information that I'm starting with...
Wikipedia: Machine learning
Vishal Maini, "Machine Learning for Humans", Medium, 19 August 2017
Samuel, A. L. "Some Studies in Machine Learning Using the Game of Checkers." IBM Journal of Research and Development 3 (July 1959): 210-229.
Mannila, H. "Data mining: machine learning, statistics, and databases." Proceedings of 8th International Conference on Scientific and Statistical Data Base Management. IEEE Comput. Soc. Press, 1996, 2-9.
Wernick, Miles, Yongyi Yang, Jovan Brankov, Grigori Yourganov, and Stephen Strother. "Machine Learning in Medical Imaging." IEEE Signal Processing Magazine 27 (July 2010): 25-38.
Langley, Pat. "The changing science of machine learning." Machine Learning 82 (March 2011): 275-279.
Simon, Herbert A. "Why Should Machines Learn?" Machine Learning. Ed. Ryszard S Michalski, Jaime G Carbonell, and Tom M Mitchell. Symbolic Computation. Springer Berlin Heidelberg, 1983. 25-37.
Steven Levy, "How Google is Remaking Itself as a “Machine Learning First” Company", Wired, 22 June 2016.
Alex Hern, "Google says machine learning is the future. So I tried it myself", The Guardian, 28 June 2016.