4 ms·

I took Isbell's class as well, and perhaps here we can share our respective experiences. In the year I did it, the class was structured as follows: At the beg

I took Isbell's class as well, and perhaps here we can share our respective experiences. In the year I did it, the class was structured as follows: At the beginning of the semester, you'd pick two datasets. Every two weeks, you'd apply two or so algorithms that were being covered at the time (maybe k-means and SVD, or a NN and SVM) to your chosen data sets. There would be a set of variations that you were supposed to apply to each algorithm. Typically you'd normalize or clean the data in some way. Perhaps you'd filter outliers, etc... The result would be a set of experiments to run (2 datasets) x (2 algorithms) x (2^3 variations per algorithm). You would compile the results into a (10 page max) paper, with analysis about how the dimensions differed. It was up to the student to figure out how to actually implement this pipeline (I used sqlite + numpy/scipy/scikitlearn, many used Matlab). On paper, this sounds like a great class - what a wonderful way to learn about how different approaches relate to each other, and how crucial the process of preparing data is to the effectiveness of the algorithm. In practice, however, this did not happen for most students I knew. These students spent most of their time finding implementations of the algorithms and hacking at them to actually run all the experiments. They then rushed through gluing the results together through some semblance of analysis. Alumni of the class I knew said the same thing about their experience. This analysis was read by TA's. There were I think 3 of them for about 100 students. We wouldn't get the papers back for weeks (long past we moved on to new material). When we got our papers back there was very little feedback of the content - mostly it was noted that we submitted the work on time, and had successfully performed all the experiments required. I agree that Isbell is a joy to listen to - he is charismatic, entertaining, and I too enjoyed his anecdotes. However, I felt like you would only get something out of his lectures if you already knew what you were talking about. When I think about the quality of the class, I think about how responsive the class is to the individual needs and progress of the student. If you say that it's up to the student what they get out of the class, and your bar for a good class is that the content is arranged in a nice manner, then here you go https://pe.gatech.edu/sites/pe.gatech.edu/files/agendas/CS-4641-Machine%20Learning%20(QUP).pdf https://pe.gatech.edu/sites/pe.gatech.edu/files/agendas/CS-4... ... any self-directed student can grab Mitchell, and do the weekly assignments I describe above - all for free and in the comfort of their own home.

aschampion 10y ago
I agree that latency and detail of feedback is an enormous problem with this sort of partially-guided coursework. However, it's a generalized problem with higher education, not specific to GT, in that when implemented effectively it's one of the most valuable education experiences but difficult to scale, because it demands time-consuming supervision. This is especially true of term project courses, where the final portion of the project to which you devote the most time and creativity is also the part for which you're likely to receive the least feedback.
nl 10y ago
These students spent most of their time finding implementations of the algorithms and hacking at them to actually run all the experiments. They then rushed through gluing the results together through some semblance of analysis. Alumni of the class I knew said the same thing about their experience. Ironically, this sounds quite a lot like much of industry.
- throwawayGT 10y ago
  Or unsurprisingly...
  - nl 10y ago
    yeah, true.
jsolson 10y ago
When I took it we were encouraged to use Weka for the algorithm implementations themselves. This certainly allowed me (and I'd never so much as touched machine learning prior to taking the class -- I took it on a bit of a lark that wasn't related to my research at all) to focus on understanding the behavior of the algorithms rather than worrying about hacking them together. I'd agree that Tech has too few TAs for too many students, generally, for its graduate courses, but I don't know that other schools do a better job. A brief survey of the folks around my desk elicited howls of laughter at the notion of useful or accessible TAs in grad school. > I agree that Isbell is a joy to listen to - he is charismatic, entertaining, and I too enjoyed his anecdotes. However, I felt like you would only get something out of his lectures if you already knew what you were talking about. I think this assertion is, at best, too strong. A better assertion might be that his lectures depended on coming in with sufficient background. As I said, I came into the course with no experience with machine learning at all. On the other hand, I did have a fairly strong theoretical computer science, stats, and linear algebra background. I will admit that may have made me blind to things he was simply assuming with respect to educational background that were not actually safe to assume. That said, I still refer back to his primer on information theory (http://www.cc.gatech.edu/~isbell/tutorials/InfoTheory.fm.pdf http://www.cc.gatech.edu/~isbell/tutorials/InfoTheory.fm.pdf) when discussing work relying on it, so he certainly made some effort to fill in gaps as he discovered they were common. > When I think about the quality of the class, I think about how responsive the class is to the individual needs and progress of the student. For a graduate level course I feel a class clears this bar when it accurately and thoroughly documents the prerequisites. Now, I'm not saying Charles's class necessarily does this. As I said, I came in with a pretty strong background in what turned out to be more than sufficient, but with that background I personally felt his lectures were quite tractable, even assuming complete ignorance of ML itself.

aschampion 10y ago

I agree that latency and detail of feedback is an enormous problem with this sort of partially-guided coursework. However, it's a generalized problem with higher education, not specific to GT, in that when implemented effectively it's one of the most valuable education experiences but difficult to scale, because it demands time-consuming supervision. This is especially true of term project courses, where the final portion of the project to which you devote the most time and creativity is also the part for which you're likely to receive the least feedback.

nl 10y ago

These students spent most of their time finding implementations of the algorithms and hacking at them to actually run all the experiments. They then rushed through gluing the results together through some semblance of analysis. Alumni of the class I knew said the same thing about their experience. Ironically, this sounds quite a lot like much of industry.

throwawayGT 10y ago
Or unsurprisingly...
- nl 10y ago
  yeah, true.

jsolson 10y ago

When I took it we were encouraged to use Weka for the algorithm implementations themselves. This certainly allowed me (and I'd never so much as touched machine learning prior to taking the class -- I took it on a bit of a lark that wasn't related to my research at all) to focus on understanding the behavior of the algorithms rather than worrying about hacking them together. I'd agree that Tech has too few TAs for too many students, generally, for its graduate courses, but I don't know that other schools do a better job. A brief survey of the folks around my desk elicited howls of laughter at the notion of useful or accessible TAs in grad school. > I agree that Isbell is a joy to listen to - he is charismatic, entertaining, and I too enjoyed his anecdotes. However, I felt like you would only get something out of his lectures if you already knew what you were talking about. I think this assertion is, at best, too strong. A better assertion might be that his lectures depended on coming in with sufficient background. As I said, I came into the course with no experience with machine learning at all. On the other hand, I did have a fairly strong theoretical computer science, stats, and linear algebra background. I will admit that may have made me blind to things he was simply assuming with respect to educational background that were not actually safe to assume. That said, I still refer back to his primer on information theory (http://www.cc.gatech.edu/~isbell/tutorials/InfoTheory.fm.pdf http://www.cc.gatech.edu/~isbell/tutorials/InfoTheory.fm.pdf) when discussing work relying on it, so he certainly made some effort to fill in gaps as he discovered they were common. > When I think about the quality of the class, I think about how responsive the class is to the individual needs and progress of the student. For a graduate level course I feel a class clears this bar when it accurately and thoroughly documents the prerequisites. Now, I'm not saying Charles's class necessarily does this. As I said, I came in with a pretty strong background in what turned out to be more than sufficient, but with that background I personally felt his lectures were quite tractable, even assuming complete ignorance of ML itself.