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CMU's Introduction to Machine Learning Course

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Re: CMU's Introduction to Machine Learning Course

#31

Post by idunning »

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.

I'm taking this class right now and it is certainly an interesting twist on the topic - its fun to see how many different ML techniques solve variants of a single base problem that you can analyze with statistical learning theory. Also: how many different regularizations are equivalent, and how some "intuitive", ad-hoc-seem-to-work regularizations you might think up in isolation actually can be theoretically justified. It contrasts with the more traditional, also grad-level 6.867 ML class.

Re: CMU's Introduction to Machine Learning Course

#32

Post by rahimiali »

graycat wrote: post #23

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 econo…

a good machine learning course might indeed cover 1940-1980s operations research (nonlinear optimization, linear/quadratic programming, dynamic programming), and statistics from 1970-1990s (graphical models, markov chain monte carlo methods, measures of model capacity). i'd say the field borrows the most useful bits from these fields and finds good honest use in many real life problems today. and i agree that there's a lot of unwarranted hype that leads to a lot of well-deserved skepticism.

Re: CMU's Introduction to Machine Learning Course

#33

Post by mimighost »

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 am currently in this course.

Honestly, this is not a course that I would recommend. The most problematic part of this course is its lack of clear outline. It jumps between different fields of machine learning, which could have fundamentally different focuses and motivations, without illustrating the connections to the students. It talks about Watson-Nadaraya classifier in the second class, then we have two lectures to explain most basic naive bayes algorithm. I just don't get it.

Though it gets me confusing a lot of times, the course is useful in a way that gives me a lot of keywords to search for and read article about.Also the homeworks might be challenging some time, working through them did improve my understanding of something I might think trival before, like the linear regression stuff.

And if you are really interested, I would recommend a book, which covered most of the materials of the course while being much more organized:

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

A refresher in linear algebra will also help~

Re: CMU's Introduction to Machine Learning Course

#34

Post by Anon84 »

MLfan wrote: post #16
Anon84 wrote: post #6

Earlier quoted context omitted.

Yes, I'm taking it now. Edit: https://www.coursera.org/course/ml

There's also a more sophisticated course on ML by Hinton: https://www.coursera.org/course/neuralnets Have you tried it as well?

I'm browsed it a bit. I'm hoping they will offer it again.

I've been following the self paced AI class in Udacity https://www.udacity.com/course/cs271

Re: CMU's Introduction to Machine Learning Course

#36

Post by YZF »

anatoly wrote: post #21
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 Cou…

I'll second that.

I'm currently doing both the Coursera course and the Caltech course concurrently. I really like the level and delivery style of the Caltech course. It covers a lot of material, with good depth and rigour where needed and with a lot of colour. Makes you want to jump and try the techniques out.

In contrast the Coursera course seems a bit easy and dry. I also dislike the dependency on Octave.

Re: CMU's Introduction to Machine Learning Course

#37

Post by syllogism »

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.

Here's the averaged perceptron used in a part-of-speech tagger: http://honnibal.wordpress.com/2013/09/11/a-good-part-of-spee...

Because the learner is quite a good fit for the task, it performs better in terms of speed/accuracy trade-off than many other algorithms, such as CRF.

A follow up post for statistical dependency parsing should be finished in about a month (it's down my queue...)

Re: CMU's Introduction to Machine Learning Course

#38

Post by espeed »

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.

Prof Yaser S. Abu-Mostafa's Caltech course "Learning from Data" (http://work.caltech.edu/telecourse) is probably the best introductory course for really understanding the physics of how machine learning works.

See Prof's Yaser's 1 min overview: http://www.youtube.com/watch?v=KlP0DpiM7Lw

The "Learning from Data Book" videos are online for free, and the book is on Amazon...

Videos: http://home.caltech.edu/lectures.html

Book: http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...

The course is also availble on EdX: https://www.edx.org/course/caltechx/cs1156x/learning-data/11...

Re: CMU's Introduction to Machine Learning Course

#40

Post by lunchladydoris »

chatman wrote: post #35

Unless it is blackboard with chalk, its no fun following online. That's why MIT's lectures rock!

Yes! I've done quite a few online courses now and the ones I've enjoyed the most have all been ones that were essentially recordings of the normal classes students take on campus.

In the case of Harvard's CS50x, it was essentially the exact same course. (Plus it helped that David Malan is an outstanding teacher.)

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