7 ms·

I did both my BSCS and MSCS at Georgia Tech. While I have many complaints about the school, the quality of the classes is not one of them, for either the underg

by jsolson 10y ago

I did both my BSCS and MSCS at Georgia Tech. While I have many complaints about the school, the quality of the classes is not one of them, for either the undergrad or grad programs. That said, with a couple of notable exceptions, the graduate classes are there for PhD students as first and second year background material so they have some starting points for their research. This naturally leads to a format where the semester can effectively be described as a long reading list of papers and lectures to spur discussion on the content of the paper. I was planning on pursuing a PhD when I started into my MS, so this format worked quite well for me at the time. In the years subsequent to that, the grounding from those classes has given me starting points for deep dives into problems I encountered at work[0]. It's interesting that you brought up machine learning. Charles Isbell's Intro ML class was a significant exception to the pattern I described above. In addition to high quality, pre-prepared lectures peppered with entertaining anecdotes, the had high quality projects that worked with pratcial tooling. It was also probably the highlight of my graduate career[1]. [0]: In particular, the material covered in my graduate systems classes has been invaluable for not reinventing the wheel for the thousandth time. The material from the couple compilers classes I took on a whim has been a huge boon when talking about software correctness. I work on the hypervisor underneath GCE. Correctness is near and dear to my heart, but performance is right there with it :) [1]: For undergrad that dubious honor has to go to Olin Shivers, not only because of his eclectic teaching style, but also because his class completely altered the way I think about problems in computer science. In particular, my mindset shifted to one of models of computation and decomposition of problems into subproblems for which the simplest model could apply. I have an example I'd like to write up, but it's a bit long for a footnote.

crusso 10y ago
Charles Isbell is still at GT? Holy cow. I think he was the teaching assistant when I was taking VAX assembly back in the late 80's when I was there. Seemed like a nice guy. It's amazing how you don't think of someone for almost 30 years, but you read their name in a comment on HN and memories come flooding in. What do those neurons do while they're waiting to be used again?
- gte525u 10y ago
  He was a associate professor in the early 00's.
jsokol1 10y ago
Hi! I'm the director of Georgia Tech's MS in Analytics program (both on-campus and online). GT's MS in Analytics degree is actually designed specifically for people who are going to go out and work in the analytics field -- it's not a pre-PhD degree, and our courses are targeted primarily at people who want to learn and apply analytics. We have an industry advisory board that helps us target course and program content, and we're constantly working to make sure our coursework is focused to the right cohort. We even have a required applied analytics practicum (both for on-campus and online students) where our students work on analytics projects for a wide range of companies and organizations. Perhaps other degrees are different, but the MS Analytics is a very practice-focused degree.
- jsolson 10y ago
  Fair enough, and I should have been a bit clearer in my original post. In my experience with the MSCS program (nearly ten years ago at this point) the core required classes were mostly well structured and would serve people well continuing onto a PhD or growing their skill set for industry. The core constituted a relatively small chunk of the overall credits required, though, and the elective courses tended to be more along the lines of what I described. I'm glad to hear that the Analytics program has a more dedicated focus on practical matters. It might be interesting to produce a series of similar (but narrower) curricula that amount to curated collections of CS classes making up degrees in Machine Learning, Systems Programming, etc. I personally really enjoyed my dartboard-oriented approach to class registration. I learned more than I've never needed to know about approximation algorithms, cryptographic theory, and compilers. Even if much of what I learned there hasn't proven itself directly useful yet, I really enjoyed learning it for learning's sake, and I think I'd have had a hard time picking up some of the gems I pulled out of that since. I also still have a hobby of proving problems NP-complete on demand as a bit of a parlor trick (within the limited scope of problems for which you can apply the small handful of patterns I've burned into my brain over the years :).
- rememberlenny 10y ago
  I'm very interested in this program. What is the best way to get in touch with you and get the syllabus material for the courses? I'm at rememberlenny at gmail.
aschampion 10y ago
> I did both my BSCS and MSCS at Georgia Tech. While I have many complaints about the school, the quality of the classes is not one of them, for either the undergrad or grad programs. I also did BS and MS at GT, and while I generally share your experiences there were 3 or 4 truly disappointing classes during my MS. They didn't ruin my overall experience, but I can see how someone could happen to have more experiences like those and fewer positive ones and come aware with a very different perception of course quality. My overall opinion of GT is mixed, but rigor or the courses is not one of my top critiques.
throwawayGT 10y ago
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

by jsolson 10y ago

crusso 10y ago

Charles Isbell is still at GT? Holy cow. I think he was the teaching assistant when I was taking VAX assembly back in the late 80's when I was there. Seemed like a nice guy. It's amazing how you don't think of someone for almost 30 years, but you read their name in a comment on HN and memories come flooding in. What do those neurons do while they're waiting to be used again?

gte525u 10y ago
He was a associate professor in the early 00's.

jsokol1 10y ago

Hi! I'm the director of Georgia Tech's MS in Analytics program (both on-campus and online). GT's MS in Analytics degree is actually designed specifically for people who are going to go out and work in the analytics field -- it's not a pre-PhD degree, and our courses are targeted primarily at people who want to learn and apply analytics. We have an industry advisory board that helps us target course and program content, and we're constantly working to make sure our coursework is focused to the right cohort. We even have a required applied analytics practicum (both for on-campus and online students) where our students work on analytics projects for a wide range of companies and organizations. Perhaps other degrees are different, but the MS Analytics is a very practice-focused degree.

jsolson 10y ago
Fair enough, and I should have been a bit clearer in my original post. In my experience with the MSCS program (nearly ten years ago at this point) the core required classes were mostly well structured and would serve people well continuing onto a PhD or growing their skill set for industry. The core constituted a relatively small chunk of the overall credits required, though, and the elective courses tended to be more along the lines of what I described. I'm glad to hear that the Analytics program has a more dedicated focus on practical matters. It might be interesting to produce a series of similar (but narrower) curricula that amount to curated collections of CS classes making up degrees in Machine Learning, Systems Programming, etc. I personally really enjoyed my dartboard-oriented approach to class registration. I learned more than I've never needed to know about approximation algorithms, cryptographic theory, and compilers. Even if much of what I learned there hasn't proven itself directly useful yet, I really enjoyed learning it for learning's sake, and I think I'd have had a hard time picking up some of the gems I pulled out of that since. I also still have a hobby of proving problems NP-complete on demand as a bit of a parlor trick (within the limited scope of problems for which you can apply the small handful of patterns I've burned into my brain over the years :).
rememberlenny 10y ago
I'm very interested in this program. What is the best way to get in touch with you and get the syllabus material for the courses? I'm at rememberlenny at gmail.

aschampion 10y ago

> I did both my BSCS and MSCS at Georgia Tech. While I have many complaints about the school, the quality of the classes is not one of them, for either the undergrad or grad programs. I also did BS and MS at GT, and while I generally share your experiences there were 3 or 4 truly disappointing classes during my MS. They didn't ruin my overall experience, but I can see how someone could happen to have more experiences like those and fewer positive ones and come aware with a very different perception of course quality. My overall opinion of GT is mixed, but rigor or the courses is not one of my top critiques.

throwawayGT 10y ago

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