
Complete Course on Machine Learning - weeha
http://www.computervisiontalks.com/tag/machine-learning-class-10-701/
======
rugatelstvo
I am under the impression that to learn statistics one must first have a
working knowledge of probability theory which rests upon grad level math
analysis. Can machine learning be studied without any of that?

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RogerL
A lot of this is done in discrete math. You know, the actual probability is
defined by this integral, but there is no closed form solution to the
integral, so we do sums to find the approximate answer. Anyone can understand
sums. And, it's probabilities, so the sums must equal one. Not _that_ hard,
right ;)

It sure helps to understand the integral equations, especially if you want to
read the original literature. But realistically you are going to need to
understand summing, normalizing, algorithms for clustering, and so on. You
probably don't want to write your own numerical code anyway; someone else did
it, and they handled all the edge cases that a naive implementation misses.

You can find PDFs of the James, Witten, Hastie, Tibshirani book "An
Introduction to Statistical Learning" [1]. Scroll on through - there is
nothing intimidating math wise. All the heavy lifting is left to R.

Jump in, the water is fine!

[1]
[http://web.stanford.edu/~hastie/pub.htm](http://web.stanford.edu/~hastie/pub.htm)

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huac
If you're serious about the math, read "The Elements of Statistical Learning'
instead. Same guys, just as much R code, but harder.

[http://statweb.stanford.edu/~tibs/ElemStatLearn/](http://statweb.stanford.edu/~tibs/ElemStatLearn/)

~~~
sarwechshar
What online beginner machine learning courses or templates would you recommend
that utilize R?

~~~
huac
I don't really know what you're looking for. If it's a replacement for that
Coursera ML class in Python, then I don't think there really is one. The basic
tenets of ML aren't going to change depending on your language, though.

Some good R-specific resources:

[http://www-bcf.usc.edu/~gareth/ISL/](http://www-bcf.usc.edu/~gareth/ISL/)
[https://cran.r-project.org/web/views/MachineLearning.html](https://cran.r-project.org/web/views/MachineLearning.html)
[http://ocw.mit.edu/courses/sloan-school-of-
management/15-097...](http://ocw.mit.edu/courses/sloan-school-of-
management/15-097-prediction-machine-learning-and-statistics-
spring-2012/index.htm)

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gamapuna
Here's the complete course:
[http://alex.smola.org/teaching/cmu2013-10-701/](http://alex.smola.org/teaching/cmu2013-10-701/)

~~~
ojaved
This is the 2013 course. The original post is for the spring 2015 machine
learning course

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joshvm
Some nice courses there, also check out Dan Cremer's lectures on variational
methods for computer vision if you're interested in that sort of thing.
There's also a nice series on computer vision for special effects.

[http://www.computervisiontalks.com/variational-methods-
for-c...](http://www.computervisiontalks.com/variational-methods-for-computer-
vision-lecture-2-prof-daniel-cremers/)

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anacleto
That's really nice. Dan Cremer is impressive.

Here's a great Laboratory on Amazon ML for Human Activity Recognition (w/
Python). [https://cloudacademy.com/amazon-web-services/labs/aws-
machin...](https://cloudacademy.com/amazon-web-services/labs/aws-machine-
learning-human-activity-recognition-21/)

Totally worth a look.

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yla92
A bit off topic : what are the best recommended way/resources to learn linear
algebra and basic probability and statistics ?

~~~
stdbrouw
For linear algebra, I like the "No BS guide to linear algebra"
([https://gumroad.com/l/noBSLA](https://gumroad.com/l/noBSLA)) which also
includes a high school math refresher for people who need it (I did).

For probability, "Probability Demystified" is a good basic intro.

For statistics, I would really recommend Allen Downey's Think Stats
([http://greenteapress.com/thinkstats2/index.html](http://greenteapress.com/thinkstats2/index.html)),
especially if you're coming from a programming background. Most introductions
to statistics focus heavily on the mathematics needed to enable certain
analytical approximations to difficult probabilistic calculations (e.g. the
t-test), whereas Think Stats just bites that bullet and focuses on simulation
/ brute force so you can spend more time on the actual fundamental theory
behind statistics.

Brian Blais' "Statistical Inference for Everyone"
([http://web.bryant.edu/~bblais/statistical-inference-for-
ever...](http://web.bryant.edu/~bblais/statistical-inference-for-everyone-
sie.html)) also looks really good, but haven't had a chance to review it in
depth.

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chrisdbaldwin
Some of the videos in the link are cut short, and the full videos are much
better. Here's a link to a playlist of the full lectures:
[https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYw...](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9)

~~~
ojaved
The videos on computervisiontalks.com are exactly the same as videos on
youtube because the site is pulling these videos from youtube. The post points
to the spring 2015 lectures. You are pointing to earlier lectures in 2014,2013

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phunehehe0
Just want to shout about this very comprehensive course by Caltech professor
Yaser S. Abu-Mostafa
[http://work.caltech.edu/telecourse.html](http://work.caltech.edu/telecourse.html)

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btown
I like his teaching style, but it seems some of the lecture videos (1.3, for
example) are cut off - very frustrating! For anyone watching nonetheless, I
recommend going into YouTube and changing the speed to 1.5x.

~~~
ojaved
The lectures on computervisiontalks are directly being taken from youtube (but
tags, navigation, bookmarking and in-video search capability is added). The
lecture 1.3 (for spring 2015 class) is exactly of the same length. However on
youtube, the lectures for machine learning class 2013 (also by Alex Smola) are
available which are of a different length.

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ericmo
I like Smola's ML book, and it's great to see a full-depth ML course online,
I'll certainly watch some videos. Other than that, the audio quality could be
better.

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zablocky
Have anyone seen some and can tell if the material is well explained?

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smilekzs
I took the course (Alex Smola's 10-701, Spring '15) in the classroom.
Personally I don't like his lecture style -- too vague, too many assumptions,
too much reliance on jargon he hasn't already explained. YMMV.

