
CMU's Introduction to Machine Learning Course - sreeix
http://alex.smola.org/teaching/cmu2013-10-701x/index.html
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capnrefsmmat
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.

~~~
samstave
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?

~~~
capnrefsmmat
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.

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stokedmartin
I also recommend videos and lecture notes by Tom Mitchell for the same course
taught in 2011[1]. His explanation to some non-trivial theoretical concepts of
ML is very coherent.

[1]
[https://www.cs.cmu.edu/~tom/10701_sp11/](https://www.cs.cmu.edu/~tom/10701_sp11/)

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fat0wl
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?).

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maurits
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...](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)

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

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

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

~~~
fat0wl
awesome he is a real pleasant & organized teacher for this kindof stuff I will
definitely take a look

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cschmidt
For comparison, here is MIT 9.520:

Statistical Learning Theory and Applications
[http://www.mit.edu/~9.520/](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.

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idunning
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.

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liquidcool
You may want to look at the self-evaluation, a qualification exam for
understanding the material:

[http://alex.smola.org/teaching/cmu2013-10-701x/slides/Intro_...](http://alex.smola.org/teaching/cmu2013-10-701x/slides/Intro_ML_Self_Evaluation.pdf)

It would have been a piece of cake in college, but I have not used that math
in a long time :-/

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graycat
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.

~~~
rahimiali
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.

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nsnick
That is not the intro course. This is
[http://curtis.ml.cmu.edu/w/courses/index.php/Machine_Learnin...](http://curtis.ml.cmu.edu/w/courses/index.php/Machine_Learning_10-601_in_Fall_2013)

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axitkhurana
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)

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anatoly
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.

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YZF
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.

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Sharma
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.

~~~
hoprocker
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.coursera.org/course/datasci](https://www.coursera.org/course/datasci)

~~~
ghaff
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.

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minimaxir
It's worth noting that very many CMU courses, from all departments, have notes
available on a publiclly-accessable course website.

Source: Am a CMU 2012 grad.

~~~
yeison
I think you meant noting* ... haha

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X4
All I can say:

1971–75: DARPA's frustration with the Speech Understanding Research program at
Carnegie Mellon University

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

~~~
lunchladydoris
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.)

