For the first time in ten years, I find myself teaching data mining in the fall. This means I need to figure out what data mining is in 2019. Naturally, my first stab at a syllabus is based on what I thought data mining was in 2009. Perhaps it's changed too little; nonetheless, I'm feeling OK with it at the moment*. I am sure the thoughtful and constructive suggestions of the Internet will only reinforce this satisfaction.
--- Seriously, suggestions are welcome, except for suggesting that I teach
about neural networks, which I deliberately omitted because I am an
out-of-date stick-in-the-mud reasons**.
*: Though I am not done selecting readings from the textbook, the recommended books, and sundry articles --- those will however come before the respective classes. I have been teaching long enough to realize that most students, particularly in a class like this, will read just enough of the most emphatically required material to think they know how to do the assignments, but there are exceptions, and anecdotally even some of thoe majority come back to the material later, and benefit from pointers. ^
**: On the one hand, CMU (now) has plenty of well-attended classes on neural networks and deep learning, so what would one more add? On the other, my admittedly cranky opinion is that we have no idea why the new crop works better than the 1990s version, and it's not always clear that they do work better than good old-fashioned machine learning, so there.
Posted at August 14, 2019 17:17 | permanent link