
Statistical Machine Learning, Spring 2016 - adamnemecek
http://www.stat.cmu.edu/~larry/=sml/
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eykanal
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.

[1]:
[http://www.cs.cmu.edu/~mgormley/courses/10701-f16/](http://www.cs.cmu.edu/~mgormley/courses/10701-f16/)

~~~
greenmountin
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.

[1]
[http://academictorrents.com/details/dd9b74b50a1292b4b154094b...](http://academictorrents.com/details/dd9b74b50a1292b4b154094b7338ec1d66e8894d)

~~~
reachtarunhere
I would add assignment solutions to your list of desirable things. Makes it
easy to keep moving after getting stuck.

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eykanal
[http://www.cs.cmu.edu/~epxing/Class/10701/](http://www.cs.cmu.edu/~epxing/Class/10701/)

[http://www.cs.cmu.edu/~aarti/Class/10701_Spring14/](http://www.cs.cmu.edu/~aarti/Class/10701_Spring14/)

[http://www.cs.cmu.edu/~./10701/](http://www.cs.cmu.edu/~./10701/)

[http://alex.smola.org/teaching/cmu2013-10-701/](http://alex.smola.org/teaching/cmu2013-10-701/)

All of the above have answer keys for the homework assignments.

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gavman
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.

[1] [http://www.cs.cmu.edu/~roni/10601/](http://www.cs.cmu.edu/~roni/10601/)

~~~
kimolas
Well, now we have 10-715 as the ML PhD-level course that corresponds to 701.
[https://www.cs.cmu.edu/~epxing/Class/10715/](https://www.cs.cmu.edu/~epxing/Class/10715/)

~~~
bronxbomber92
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).

Edit: Whoops, forgot the actual link: [http://www.ml.cmu.edu/teaching/ml-
course-comparison_11.2015....](http://www.ml.cmu.edu/teaching/ml-course-
comparison_11.2015.pdf#Intro%20Course%20Document)

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pizza
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)

~~~
anonymousDan
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?

~~~
pizza
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.

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adamnemecek
The professor is Larry Wasserman of the "All of statistics" fame.

~~~
placebo
Heard good things about it - thanks for pointing the connection

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capkutay
Interesting...seems like all the content is available. Last I remember, you
needed a CMU ID to access the course materials.

~~~
WhitneyLand
They don't have to care about giving content away.

The golden goose is the degree which is kept in artificially short supply and
very expensive.

~~~
reachtarunhere
That is a true blessing in disguise.

~~~
WhitneyLand
How so?

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thebouv
I looked at assignment #1 and realized just how dumb I am (not a bad thing,
just a statement).

I am interested in Machine Learning, but I'm going to seek out the intro
material first and come back to this (much later).

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ge96
Damn 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

~~~
imh
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.

~~~
ge96
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.

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abysmallyideal
How is it that many of these websites haven't learned about HTTPS, I.e. data
integrity?

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thro1237
Is there similar introductory courses on deep learning?

~~~
bronxbomber92
Nando de Freita's "Deep Learning" course taught at Oxford, 2015.

[https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)

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Noseshine
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":

Stanford Online: Statistical Learning

[https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about)

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."

List of courses:
[https://lagunita.stanford.edu/](https://lagunita.stanford.edu/)

~~~
ChristianGeek
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.

