CMU's Introduction to Machine Learning Course • Hacker News Board

Re: CMU's Introduction to Machine Learning Course

#21

Post by anatoly » Mon, Nov 11, 2013, 9:11 PM UTC

axitkhurana wrote: post #13

How is this course compared to Andrew Ng's Coursera class, his regular Stanford class and Caltech's Learning from Data course? (Other ML courses available on the web in terms of depth)

I will say that I'm a huge fan of the Caltech Learning from Data course (currently also offered on EdX). I took Andrew Ng's Coursera course 2 years ago, finished it successfully, and liked it. But I feel that the Caltech course gave me a much deeper foundational understanding of the basic issues and tradeoffs, and much deeper insight into what's going on.

Homework is much better in the Caltech course, too. In the Coursera course, they give you programs and environments in Octave that are all prewritten for you, and you just need to plug in a few key lines (often there's essentially one way to do it due to dimensionality). You feel like you understand what's going on, but the understanding is not really grounded. The Caltech course has multiple choice questions, but they look like this: "implement this algorithm, run it through a data set chosen randomly with such and such parameters, calculate learning error, do all this 1000 times and average. What value out of these 5 is your learning error closest to?". You choose the language, you implement the algorithm from scratch, you debug the hell out of it, you visualize your data to understand what's wrong... then the knowledge and the understanding stay with you.

Re: CMU's Introduction to Machine Learning Course

#22

Post by ghaff » Mon, Nov 11, 2013, 9:37 PM UTC

hoprocker wrote: post #17

Sharma wrote: post #4

I think now it is the time we get some tutorial/resources/classes on practical implementation of these ML techniques. Enough of Introduction to ML. How to handle large data (say 6000000 rows), how to convert csv/tbv data to different formats needed for different machine learning libraries for e.g. Weka, LibSVM etc.

For an introduction to the broader realm of data input, normalization, modeling, and visualization -- in which ML plays but a part -- you can "preview" Bill Howe's "Introduction to Data Science" class on Coursera[0]; I'm working through the lectures, and I find he gives compelling explanations of what all these parts are, why they're important, and how it all fits together in a larger context. [0] https://www.courser…

I took Prof. Howe's course on Coursera and it's a bit of a mixed bag. I can actually see it being better in some respects just going through the content after-the-fact than taking the course as it was run as there were a number of issues with auto-grading of assignments and some of the specific tools choices (like Tableau, which only runs on Windows).

That said, the course covered a lot of ground and touched on a number of different interesting/important topics. Some of the lecture material was a bit disorganized/had errors and didn't flow all that well from one topic to another but there was a lot of good material there, especially if you had enough background to appreciate it. I was comfortable enough but it was obvious that the expectations set by the prereqs were off.

Hopefully the course will run again with most of the kinks worked out and, perhaps, a better level-setting of what's needed to get the most out of the course.

Re: CMU's Introduction to Machine Learning Course

#23

Post by graycat » Mon, Nov 11, 2013, 10:30 PM UTC

Gee, guys, looking at the list of topics, a huge fraction, likely over 50%, of the material goes back to programs in operations research, statistics, and the mathematical sciences from about 1970 on. Nearly the only thing new is the collection of sample applications. From what I've seen, the quality of the content of the current ML courses is way below that going back to 1970.

Warning: History shows that the US economy looked at the material in operations research, statistics, and the mathematical sciences and rolled their eyes, did a big upchuck, laughed, turned, and walked away. One might look for alarms from their hype and fad detectors.

Re: CMU's Introduction to Machine Learning Course

#24

Post by maurits » Mon, Nov 11, 2013, 10:38 PM UTC

fat0wl wrote: post #5

I did the Stanford free online one the first time it was offered a year or so back. Was perfect -- didn't move at a blazing pace and was very lean. Great instructor, highly recommended (though I think it may have been absorbed into Coursera?).

If you liked the ML Coursera class, Ng also has an introduction to deep-learning in more or less the same casual explicit style.

[1]: Wiki with code, exercises and explanation

[2]: Video lecture one with a recap on backprop

[3]: Video lecture two on Sparse Auto Encoders

[4]: Handouts

[1]: http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutori...

[2]: http://www.stanford.edu/class/cs294a/video1.html

[3]: http://www.stanford.edu/class/cs294a/video2.html

[4]: http://www.stanford.edu/class/cs294a/handouts.html

Re: CMU's Introduction to Machine Learning Course

#25

Post by fat0wl » Mon, Nov 11, 2013, 10:52 PM UTC

maurits wrote: post #24

fat0wl wrote: post #5

I did the Stanford free online one the first time it was offered a year or so back. Was perfect -- didn't move at a blazing pace and was very lean. Great instructor, highly recommended (though I think it may have been absorbed into Coursera?).

If you liked the ML Coursera class, Ng also has an introduction to deep-learning in more or less the same casual explicit style. [1]: Wiki with code, exercises and explanation [2]: Video lecture one with a recap on backprop [3]: Video lecture two on Sparse Auto Encoders [4]: Handouts [1]: http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutori... [2]: http://www.stanford.edu/class/cs294a/video1.html [3]: http://…

awesome he is a real pleasant & organized teacher for this kindof stuff I will definitely take a look

Re: CMU's Introduction to Machine Learning Course

#26

Post by samstave » Tue, Nov 12, 2013, 12:09 AM UTC

capnrefsmmat wrote: post #3

I just showed this link to three classmates who are currently taking the course, and the common reaction was "It's a trap!" They haven't been very satisfied with it. It's co-taught by two professors, one who teaches like it's an introduction for people who have never heard of Bayes' theorem and one who teaches like it's a graduate seminar for people who've seen it all before.

As someone who has never heard of Bayes theorem, is this good or bad? So some things would be explained well and other things over my head?

Re: CMU's Introduction to Machine Learning Course

#27

Post by 11001 » Tue, Nov 12, 2013, 12:10 AM UTC

cschmidt wrote: post #19

For comparison, here is MIT 9.520: Statistical Learning Theory and Applications http://www.mit.edu/~9.520/ I think it is interesting how different ML courses can have such different emphasis in content. The MIT course is all about regularization.

Part of the course requirement seems to be improving the quality of relevant Wikipedia articles: http://www.mit.edu/~9.520/fall13/projects/Projects2013.pdf

Re: CMU's Introduction to Machine Learning Course

#28

Post by [deleted] » Tue, Nov 12, 2013, 1:27 AM UTC

samstave wrote: post #26

capnrefsmmat wrote: post #3

I just showed this link to three classmates who are currently taking the course, and the common reaction was "It's a trap!" They haven't been very satisfied with it. It's co-taught by two professors, one who teaches like it's an introduction for people who have never heard of Bayes' theorem and one who teaches like it's a graduate seminar for people who've seen it all before.

As someone who has never heard of Bayes theorem, is this good or bad? So some things would be explained well and other things over my head?

[deleted]

Re: CMU's Introduction to Machine Learning Course

#29

Post by psycovic23 » Tue, Nov 12, 2013, 3:36 AM UTC

capnrefsmmat wrote: post #3

I just showed this link to three classmates who are currently taking the course, and the common reaction was "It's a trap!" They haven't been very satisfied with it. It's co-taught by two professors, one who teaches like it's an introduction for people who have never heard of Bayes' theorem and one who teaches like it's a graduate seminar for people who've seen it all before.

I took it last semester. Most definitely a trap - Smola is one of those guys who's just too smart to teach. Great material - terrible instruction.

Re: CMU's Introduction to Machine Learning Course

#30

Post by capnrefsmmat » Tue, Nov 12, 2013, 3:41 AM UTC

samstave wrote: post #26

capnrefsmmat wrote: post #3

I just showed this link to three classmates who are currently taking the course, and the common reaction was "It's a trap!" They haven't been very satisfied with it. It's co-taught by two professors, one who teaches like it's an introduction for people who have never heard of Bayes' theorem and one who teaches like it's a graduate seminar for people who've seen it all before.

As someone who has never heard of Bayes theorem, is this good or bad? So some things would be explained well and other things over my head?

Right. Some things would be explained from the basics, and some topics would be covered by referring you to obscure papers on advanced techniques in machine learning published by the professors.