throwawayGT

The question would be whether the time spent on this program compares to what one would be able to accomplish on their own, and whether the difference is worth the price tag.

From my experiences with GT, I am apprehensive about the quality of content - I assume it's coming from the same departments and professors that I had experience with.

From other comments here it seems that the approach this program took was quite different from the one employed on campus, so that apprehension may be unfounded - that is, the online offerings may be of higher quality than what students on campus receive. Still, I felt like I needed to post something to warn people that the branding of GT does not in-and-of-itself mean that the content will be of high quality. Students considering the program should try and find some way to evaluate this - are there sample classes or lectures posted online? Perhaps ask someone you trust in the industry to take a look and give you their feedback.

Still, I think students who are self-motivated should consider what they would be able to accomplish if they took some time to organize a study program for themselves. There are many free high-quality resources out there that could be used for effective self-directed study.

Students who are less confident about doing it on their own should be asking themselves what sort of support they expect to be getting from the program. Certainly there are many advantages in having things curated for you, as well as having access to discussion boards with other students going through the same material. Aside from that, many students (unfortunately, I think), need the external schedule and commitment - and for them, merely having an exam deadline, or the $10K investment looming in the background may be the thing needed to get through the material. Those students, too, should be realistic about the investment they are making and what they hope to get out of it.

My criticism is precisely that feedback was lacking. The assignments were only graded on submission - there was no feedback there (likely because every student worked with different data so going in-depth would have required the grad student TAs to spend too much time per student digging in).

I don't agree that feedback during lecture is valuable or low-latency as you say - not with 100 students attending. It might work to ask a clarifying question here and there, but again - you're only in a position to take advantage of that if you're already comfortable with the material and are generally keeping up.

Books are different than lectures, sure, but I don't think there's much difference between attending a lecture with 100 students, or watching one online. Indeed many people claim the online way is better, since you can rewind and skip around, pause and lookup references, etc...

Or unsurprisingly...

I do agree that having materials that provide an approach to a topic is very useful, but as I mention elsewhere such materials are available for free online.

You can find the syllabus for Isbell's class and follow along. You can do the readings and programming investigations. If you like lectures, you can find many full courses on YouTube (I found caltech's lectures https://www.youtube.com/watch?v=eHsErlPJWUU to be the best at presenting SVM's out there, although this was probably my third attempt at understanding them so maybe the other resources rubbed off.. they also skim over the quadratic programming detail but I get that this may be beyond the detail that many people desire in an intro class).

If you have to teach the material to yourself, how is your experience improved by being in the class?

I haven't gone through other programs, so I can't say from personal experience.

There are no programs that have stood out to me during hiring to predict the quality of the candidate.

If you're a student looking at programs, I would prioritize the amount and quality of individual attention you stand to receive. A self-directed student can do well anywhere (including on their own). If you're not so stubborn/resilient, having a good community and mentorship to help you overcome difficult times is key.

I am bitter about having spent time, money and emotional energy on something that I felt I got no value out of, and I want to warn people thinking about doing the same.

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-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.

I'm an engineer that does hiring as part of my duties. I don't ever look at the degree.

To signal your skills, I'd think about the industry you want to work in, and try and work on a project that is similar to work you'd like to do.

For example, if you're wanting to get into Data Science, find a data set, pick a question and answer it. Build visualisations, implement ML algorithms, etc. Put your code up online and write a blog post (or several) about the process.

A year spent doing that would be worth more in my eyes than a MS.

I was on the Machine Learning track. Already had a CS degree from a prestigious school. I took classes primarily in the ML concentration, although I had to take some courses in theoretical cs and they were similar.

I'd say my peers generally shared my opinion (classes not being very good). Many of them were trying to get into PhD programs so they were focusing on finding research opportunities and didn't care about the quality of classes much. Some struggled but blamed themselves for this rather than the class. (I'd say this was very common amongst the undergrads too).

Of the PhD students I knew, most were discouraged from taking classes altogether, since it took away time from research. This was true even in the first year of their program. The general attitude from that side was that classes were a waste of time.

Many higher-level classes were run as mini-research projects. You'd get some content, then the rest of the class would be forming teams, proposing project ideas, implementing, writing up results and having 'mini conferences'. I think faculty liked this since it was a good way to try out research ideas, recruit potential PhD students, and give their current students extra time to work on their research.

Nothing wrong with that format, of course, but the actual coverage of content was typically superficial. If you weren't already familiar with the area, you had to figure it out on your own as you went along. Also, this is not a class format that translates to online very well.

As someone who completed a (on-campus) CS masters at GT, I really wish I didn't. The classes were of very poor quality - it was clear that they were a low priority for most faculty. Andrew Ng's Coursera class on Machine Learning was the pedagogical highlight of my time at GT, and I did it on my own initiative (and it's free).

I know people have many reasons to get a Masters. If your goal is to take some higher-level classes, you can do better than GT. If you are self-motivated enough to do an online degree, you can do it for free. Aside from free offerings from sites like Coursera, you can find whole courses up from many institutions - with syllabi, lecture slides, homework assignments, etc.

If you're planning to do it for the salary, in my experience the degree ended up being worth a $95K to $115K bump in starting salary. Compare this with the 2 years of industry salary that I would have received, and the 2 years of experience (and raises that come with that). I know I wasn't paid better than the folks who had been with the company for 2 years more than me.

If you're thinking about it for the sake of your resume, I do hiring screening / interviews now (for Data Science and Software Engineering positions) - and I really don't care if the applicant has an MS or not (or what classes they may have taken). Most folks I know that do hiring think similarly. My main signal from your resume is projects you've been on and how you contributed.

From my point of view, this program is a losing proposition for any potential student.