Is there similar introductory courses on deep learning?
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
Statistical Machine Learning, Spring 2016
31–40 of 45 posts
Re: Statistical Machine Learning, Spring 2016
#32For 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 mater…
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.
Re: Statistical Machine Learning, Spring 2016
#33For 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 mater…
The syllabus says prerquisites are 10-701 and 36-715.
Could you say something about 36-715? I can't seem to find any details.
Re: Statistical Machine Learning, Spring 2016
#34Damn it, maybe machine learning isn't for me. I took this class CSE-191 and I sucked bad at it, similar to this with proofs, logic statements. Fuck
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.
Re: Statistical Machine Learning, Spring 2016
#35I 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": Stanford Online: Statistical Learning https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta... Quote: "This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The s…
I took it graded over this summer, and it really helped me gain a deeper perspective in statistics. I'd definitely recommend it
Re: Statistical Machine Learning, Spring 2016
#36For 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 mater…
The syllabus says prerquisites are 10-701 and 36-715. Could you say something about 36-715? I can't seem to find any details.
[deleted]
Re: Statistical Machine Learning, Spring 2016
#37How is it that many of these websites haven't learned about HTTPS, I.e. data integrity?
Re: Statistical Machine Learning, Spring 2016
#38Kevin 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?
Re: Statistical Machine Learning, Spring 2016
#39Damn it, maybe machine learning isn't for me. I took this class CSE-191 and I sucked bad at it, similar to this with proofs, logic statements. Fuck
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.
Re: Statistical Machine Learning, Spring 2016
#40Kevin 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.