Basics of Machine Learning

http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html

by luu • 12 years ago

253 32 12 years ago

homepages.inf.ed.ac.uk

Legoben• 12 years ago

Any chance we can get the lectures before #5?

mrknmc• 12 years ago

Here's a link to the course website: http://www.inf.ed.ac.uk/teaching/courses/iaml/ I took this course and Professor Lavrenko is one of the best lecturers at the uni.

arethuza• 12 years ago

Interesting to see that Edinburgh is using US style position naming in addition to more traditional UK positions (i.e. "Lecturer" == "Assistant Professor", "Reader" == "Associate Professor" etc.)

peanut_merchant• 12 years ago

I did the course several years ago when I studied there. He is excellent. Also recommend his text technologies course (lecture notes but no videos here) : http://www.inf.ed.ac.uk/teaching/courses/tts/

imurray• 12 years ago

The main course website is here: http://www.inf.ed.ac.uk/teaching/courses/iaml/

There are more notes and so on there. It isn't an online course though, so don't expect too much.

edran• 12 years ago

We also have two more in-depth (and harder) courses on ML with publicly available slides:

* Machine Learning and Pattern Recognition: http://www.inf.ed.ac.uk/teaching/courses/mlpr/

* Probabilistic Modelling and Reasoning: http://www.inf.ed.ac.uk/teaching/courses/pmr/

Unfortunately video lectures are often only available internally.

catkin• 12 years ago

I didn't take it myself, but my (now former) classmates that took PMR told me it was one of the hardest courses they ever did.

petulla• 12 years ago

I'm sure these lectures are great but my computer was none too happy about 100 embedded videos in a single Web page.

nhebb• 12 years ago

Yeah, my machine learned not to visit that page again.

visarga2• 12 years ago

And they don't have controls (full screen, volume, seek).

jds375• 12 years ago

I also highly recommend these lectures from Cornell. The lecturer is well-known for his free SVM-light implementation. http://machine-learning-course.joachims.org

3pt141592• 12 years ago

If you want to get into machine learning, it is actually pretty easy, provided you studied Math, Engineering, or Science in University. The papers are hard at first, but they don't hide like other papers do. Everything is laid out there for you to code and run with your own data. In a field like, say, Structural Engineering, the paper writers can make claims about the structural resilience of an Ultra High Performance Concrete that they tested, without you ever being able to hope to repeat the exact experiment. You may not even be able to get your hands on the proprietary compound they used.

In ML, you might not be able to use the same corpus / training set, but you're usually able to recode the actual algorithm and you're usually able to find a compatible type of data set to work off of.

Also, most ML people are lazy, so if they don't work for Google or Facebook they're usually using open data datasets anyway, which are trivial to drop in and verify.

visarga2• 12 years ago

most ML people are lazy

That's a great virtue for programmers in general.

"Laziness: The quality that makes you go to great effort to reduce overall energy expenditure. It makes you write labor-saving programs that other people will find useful, and document what you wrote so you don't have to answer so many questions about it. Hence, the first great virtue of a programmer. Also hence, this book. See also impatience and hubris. (LarryWall, ProgrammingPerl, 1st edition)"

they're usually using open data datasets anyway

They do that in order to be able to judge the performance of various algorithms - they need to have a common standard to test against.

3pt141592• 12 years ago

I use lazy in the positive sense, I'd actually forgotten that it had negative connotations when I wrote the above.

mandeepj• 12 years ago

Bill Gates once said - "I choose a lazy person to do a hard job, Because a lazy person will find an easy way to do it."

source - http://en.wikiquote.org/wiki/Talk:Bill_Gates#Lazy_person_to_...

nsomaru• 12 years ago

I'm curious -- does one need to consult these papers when using algorithms from libraries such as SciKit (Python)?

I ask because the field seems so broad, and having completed Andrew Ng's original ML course, I'm not sure which way to go...

thanks.

agibsonccc• 12 years ago

Hi Machine learning instructor from Zipfian Academy here.

We teach scikit learn at our bootcamp and people who have some sort of scientific background, have completed Ng's course, or are software engineers typically apply to our program.

What we have found in the class room is that if you understand the fundamentals of machine learning algorithms, you don't have to understand all the minutiae of an algorithm as a user.

The hardest problem to solve is figuring out how to encode data such that the algorithm learns properly as well as all the pre processing steps that typically go in to machine learning.

The only thing that would be worth understanding for an algorithm are the hyperparameters associated with the respective models. An example of this would be learning rate or regularization coefficients.

If you understand what those are useful for, you should be fine.

nandemo• 12 years ago

FWIW, I'm studying ML with Introduction to Statistical Learning, which was recommended to me by several people.

Personally I didn't enjoy Coursera's ML course (though I believe Ng is a great teacher) and dropped out, but I'm enjoying this book.

It's available as PDF for free:

http://www-bcf.usc.edu/~gareth/ISL/

icelancer• 12 years ago

Also, most ML people are lazy

As someone who has written a fair amount of machine learning code, I could not agree more. It is an inelegant way to solve a problem by brute force.

Which, of course, is occasionally very useful! But it is often lazy.

Though it sounds really cool.

graycat5• 12 years ago

Best I can tell, it comes in a Champagne bottle with a cork held in by wire and a nice, new label, but, when open the bottle and pour out the contents, what get is 99 44/100% pure, old cookbook-style, applied statistics, some curve fitting, some hypothesis testing, some statistical estimation with definitions, theorems, and proofs mostly filtered out. Also not much in experimental design and only little in 'resampling' techniques.

Right: Since we need a computer to do the data manipulations, the computer science people want to conclude that the statistics is also part of 'computer science'? Now bookkeeping, accounting, numerical solutions of differential equations, etc. are also part of 'computer science'? Or, what the heck ever happened to the field of statistics, biostatistics, quality control, etc.?

seizethecheese• 12 years ago

Software eating the world.

Houshalter4• 12 years ago

What's your complaint exactly? There is a lot of overlap with statistics, sure. Especially in the simpler stuff. Machine learning is literally using machines to automatically create models. What did you expect?

graycat5• 12 years ago

Machine learning is literally using machines to automatically create models.

Good we got that clear. In statistics, the 'models' have hypotheses that should be satisfied. Checking the hypotheses and, also, the model that gets built, is tricky work not much mentioned in the book and, really, super tough to program in general.

What's your complaint exactly?

Old wine, filtered, in new bottles.

I believe that students would be better advised to go for the original stuff in courses/books on statistics, the field itself. Yes, then a computer can do the data manipulations.

There has long been a threat of 'cook-book' statistics where get some software and a 'tool', apply it where some crucial hypothesis does not hold, and then get misleading results. Do this in medicine and can kill people; indeed, instead, actually, biostatistics is a relatively serious subject.

Well, it seems fairly clear that with 'machine learning' the threat of 'cook-book' statistics is much greater.

Or, there long was threat enough with SPSS, SAS, R, Matlab, etc., but now the threat is greater.