For those of you interested in this stuff, this course (10-702) is the second in a series. The 10-701 course, "Intro to Machine Learning", is a fantastic course as well, even if just for the exercises. This year's version is here [1], you can find lecture notes, links to lectures posted on YouTube, homeworks, readings, etc. You can also just google "10-701" and see a lot of previous course websites with similar material.
For what it's worth, nowadays, the problem isn't availability of learning material. This stuff is being literally given away. Its that you, the student, has to really dedicate time to the material. The first homework assignment for the 10-701 class wasn't even that difficult (relatively speaking) and it still took me over ten hours to finish. Persevere! It's worth it.
Thanks! One thing that can definitely set a course apart is having advanced topics + downloadable videos + captions. I really enjoyed the Stanford NLP CS224d videos, which hit all 3 and even have their own torrent [1].
Does anyone know if there's a platform for crowdsourcing video captions, maybe from the anime world?
Edit: it appears as though you can correct the auto-generated captions on Youtube videos (perhaps only if you're the owner). What a great way to get labeled Speech Recognition data for free.
Can't echo this more. The Scala Functional programming course on Coursera is another such example. I am yet to finish the assignments, 2 months after enrolling.
OTOH, the deep learning course from Udacity was a cakewalk in comparison.
I wonder if it makes sense to build a good recommender for MOOCs based on a student's appetite for challenge. Something like the adaptive questions on GRE but for MOOCs.
What I'm wondering is: where do I go on the internet to find other people who are interested in studying and discussing a particular online class? Or if that doesn't exist, should it, and could it be a good thing? stackoverflow / quora / r/math etc would still be the place for carefully-asked generally relevant questions, but it seems like it would be really helpful and motivating to have place where you could contact other people who are studying or otherwise knowledgable about the same course. It could be a mailing list, or a subreddit or whatever. Ideally it would also provide a way to put together a group of people to start on a new course. Does anything like this exist already?
Recent CMU alum here ('15). 10-701/702 are the intense higher level courses meant for ML PhD students. If you want something a little less mathematically rigorous and a little more application focused, consider the masters version of the course, 10-601. All if its material is available here online as well [1]. That said, if you are willing to put in the effort and have the mathematical background to tackle 701/702, you definitely won't leave disappointed.
Here's a comparison of the courses. Both are aimed at PhD students, one for students in the Machine Learning Department and one for students in other departments (including CS).
Kevin Murphy - "Machine Learning: A Probabilistic Perspective" is a great (literally - 1000+ pages) textbook that is basically self-contained (pre-reqs: some comfort w. multivariable calculus, linear algebra, basic computer science theory; convex optimization experience a huge plus)
Yikes, quite the tome. Looks great though, I've been looking for something relatively self contained. Does it have exercises for each chapter, and if so are solutions also available?
Every chapter has exercises. One example from Ch. 14 - Kernels is this:
> Exercise 14.2 Linear separability
> (Source: Koller..) Consider fitting an SVM with C > 0 to a dataset that is linearly separable. Is the resulting
decision boundary guaranteed to separate the classes?
etc. Many exercises are proofs or derivations, and the book is full of (algorithm/optimization) defining/bounds approximation/ otherwise pragmatic information.
It seems like in general universities are moving in the direction of openness. It makes sense, the more people associate the university with "learning" the more prestige the university gets. I think that Andrew Ng's Coursera class really cemented the Stanford and ML association for a lot of people.
Class of 2012 CMU student here. The availablility of course materials varies strongly by teacher/department. All my CS and statistics course materials were accessible by public internet, but all my business/economics class materials were only available on Blackboard (ugh).
Back in school, the harder proof based math courses were a big change for me. They took a while to get the hang of and even more years to really appreciate. Stick with it and you can get good at it. It's not innate.
It just like... I don't know normal math it "works" you can work through it step by step, this stuff is almost like philosophy to me... where it didn't really make sense, you just took it for word what some guy thought. I did badly in philosophy but did well in psychology.
not saying this doesn't follow a set of rules/logic, I'm just saying I look at it and it's not like rote-memory math, you know, you look for these patterns, practice this method/approach and solve the problem...
yeah also it's a matter of passion too... I'm not actually sure what I'm passionate about, I thought I knew... but things like AI, Machine learning, computer vision, it's cool, but would I actually obsess over it and master it... I'm not sure. I'm still trying to solve the problem of "I need money" and I try to come up with ways to make a lot at once somehow, but not succeeding.
check out cs224d and cs231n (both stanford) , there's another course by university of waterloo https://uwaterloo.ca/data-science/deep-learning , you will find lecture videos for all on youtube
I bookmarked three weeks ago (busy with a few other edX and Coursera courses), and the page has a link to a free PDF book "An Introduction to Statistical Learning, with Applications in R":
Quote: "This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical)."
"This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter."
I'm working my way through this right now and highly recommend it. The book is excellent and the professors are personable and genuinely enthusiastic about the subject matter.
For what it's worth, nowadays, the problem isn't availability of learning material. This stuff is being literally given away. Its that you, the student, has to really dedicate time to the material. The first homework assignment for the 10-701 class wasn't even that difficult (relatively speaking) and it still took me over ten hours to finish. Persevere! It's worth it.
[1]: http://www.cs.cmu.edu/~mgormley/courses/10701-f16/