CMU's Introduction to Machine Learning Course (opens in new tab)

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...

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.

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~

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?

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

Nope, it's still around on youtube and on the Stanford Engineering Everywhere site. The coursera version of the class is much more introductory and skips significant parts of the full Stanford version.

http://www.youtube.com/watch?v=UzxYlbK2c7E http://see.stanford.edu/see/courseInfo.aspx?coll=348ca38a-3a...

Yes, I'm taking it now.

Edit: https://www.coursera.org/course/ml

Do you have a link? I would be interested.

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.

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

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.

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.

I can't say how this specific CMU course compares with Ng's online course, but I can comment on Ng's course as compared to several other similar courses I took in Nanyang Tech. U. and Paul Sabatier (Toulouse 3), and my overall remark is that Ng's course is quite short on the maths, which makes it not sufficiently formal to deeply understand what goes on. However, it gives enough material and code samples to play with data. It can be nicely complemented with some self-study.

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

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...)

I think you meant noting* ... haha

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.)