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A Course in Machine Learning

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Re: A Course in Machine Learning

#11

Post by zenbowman »

Smerity wrote: post #8

For those who might not know, Hal Daumé III is a highly respected researcher in machine learning, spending a lot of time working in Natural Language Processing (NLP). He also contributes his knowledge to many interesting open source projects. After creating an algorithm called SEARN for solving hard structured prediction problems, Daumé et al. went and created a practical and open implementation of the algorithm in V…

Nice. He was a fellow PhD student at USC/ISI, I remember him being the dude who always walked around barefoot.

Seems like he's done really well since, I will definitely take a look at this.

Re: A Course in Machine Learning

#12

Post by dominotw »

sampo wrote: post #9
kyro wrote: post #5

Earlier quoted context omitted.

Is this worth going through over picking up a textbook or two? I've found that Coursera courses are actually quite bloated. Lots and lots of empty talking, and very little substance.

I've taken the Coursera ML class. It's very easy if you have the adequate math background. And it's not very comprehensive, there are lots of machine learning methods that are not covered. So it's more like an introductory course to machine learning. But it's absolutely commendable how Andrew Ng takes the topic to such an understandable level that a clever high schooler who knows a little about programming could take…

>adequate math background

Do you know what kind of math is needed other than linear algebra.

Re: A Course in Machine Learning

#13

Post by Discordian93 »

dominotw wrote: post #12
sampo wrote: post #9

Earlier quoted context omitted.

I've taken the Coursera ML class. It's very easy if you have the adequate math background. And it's not very comprehensive, there are lots of machine learning methods that are not covered. So it's more like an introductory course to machine learning. But it's absolutely commendable how Andrew Ng takes the topic to such an understandable level that a clever high schooler who knows a little about programming could take…

>adequate math background Do you know what kind of math is needed other than linear algebra.

Haven't done the course, but from the preview of the videos, they cover the math you need and it's just basic, high-school level knowledge of linear algebra.

Re: A Course in Machine Learning

#14

Post by sampo »

dominotw wrote: post #12
sampo wrote: post #9

Earlier quoted context omitted.

I've taken the Coursera ML class. It's very easy if you have the adequate math background. And it's not very comprehensive, there are lots of machine learning methods that are not covered. So it's more like an introductory course to machine learning. But it's absolutely commendable how Andrew Ng takes the topic to such an understandable level that a clever high schooler who knows a little about programming could take…

>adequate math background Do you know what kind of math is needed other than linear algebra.

Basic vector and matrix operations. The first half of a typical freshman linear algebra course is more than enough. But like I said, there is a matrix review in the beginning, so if you're willing to study those extra lectures, then almost no prior knowledge is needed.

Also being able to take derivatives helps in a couple of places, but is not necessary.

Re: A Course in Machine Learning

#15

Post by denzil_correa »

I wish more people followed what Hal writes in here :

> A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible (or even advisable) to avoid math. But math should be there to aid understanding, not hinder it.

No book (yes not even PRML - Bishop), follows this diligently.

Re: A Course in Machine Learning

#17

Post by rm999 »

kyro wrote: post #5
tga wrote: post #3

If you are interested in this you might want to also look at Andrew Ng's (Stanford) Machine Learning course that is starting soon on Coursera. https://www.coursera.org/course/ml

Is this worth going through over picking up a textbook or two? I've found that Coursera courses are actually quite bloated. Lots and lots of empty talking, and very little substance.

For a beginner to machine learning I'd recommend Andrew Ng's course notes and lectures over any textbook I've seen. But I prefer his Stanford CS 229 notes to Coursera for exactly the reasons you state: they are watered down. After you really can understand Andrew Ng's course notes I'd recommend a textbook because they go in more detail and cover more topics. My two favorites for general statistical machine learning are:

* Pattern Recognition and Machine Learning by Christopher M. Bishop

* The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman

Both are very intensive, perhaps to a fault. But they are good references and are good to at least skim through after you have baseline machine learning knowledge. At this stage you should be able to read almost any machine learning paper and actually understand it.

Re: A Course in Machine Learning

#18

Post by rm999 »

denzil_correa wrote: post #15

I wish more people followed what Hal writes in here : > A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible (or even advisable) to avoid math. But math should be there to aid understanding, not hinder it. No book (yes not even PRML - Bishop), follows this diligently.

This is a harder problem than it sounds, and something I've given a lot of thought to. I think the underlying issue is that all machine learning was discovered through a combination of applied math and intuitive ideas/models. Without the intuitive model no one would have thought to discover the method, and without the math the intuitive idea would be a pipe dream. Both are fundamentally linked, and it's a bad idea to separate them.

For example, one of the most basic and oldest statistical methods is linear regression. The first thing anyone will tell you when they are teaching it is the basic "idea and model" - finding a line that fits a scatter plot (and then extending that idea). But this doesn't give you a real understanding of linear regression: where does that line come from, and why does a clean, algorithmic solution exist? Why does the standard solution often lead to numerical errors, and why is regularization a valid solution?

These questions require an increasing amount of math, but they are essential to really understanding linear regression. I agree that some textbooks just throw you a wall of math, but many actually do a solid job of explaining what is going on as they do so.

If you don't want to know the details of machine learning methods, which are inherently mathematical, you might as well as just remember the names of libraries that implement the solutions for you.

Re: A Course in Machine Learning

#19

Post by mikevm »

Discordian93 wrote: post #6

Edx is also offering the caltech ML course in mooc format: https://www.edx.org/course/caltechx/cs1156x/learning-data/11...

Note that this is not a watered-down course! It's the same course as the students at Caltech are taking, and in fact they will have the option not to attend the lectures and watch them online instead. The homework assignments will be the same ones as well (AFAIK).

Re: A Course in Machine Learning

#20

Post by denzil_correa »

rm999 wrote: post #18
denzil_correa wrote: post #15

I wish more people followed what Hal writes in here : > A second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible (or even advisable) to avoid math. But math should be there to aid understanding, not hinder it. No book (yes not even PRML - Bishop), follows this diligently.

This is a harder problem than it sounds, and something I've given a lot of thought to. I think the underlying issue is that all machine learning was discovered through a combination of applied math and intuitive ideas/models. Without the intuitive model no one would have thought to discover the method, and without the math the intuitive idea would be a pipe dream. Both are fundamentally linked, and it's a bad idea to…

You are right, it is indeed difficult and I never meant to say it is easy. My tripe was with the people who intentionally make it difficult. The thing is that you can learn ML with minimal (not minimum) math and that is what should be tried to achieved. I would be curious to know the textbooks you talk about - I have read quite a lot of those and struggled with them.

For example - Linear regression is just brilliantly tackled by Andrew Ng in his ML course (CS229 at Stanford Lecture Notes - not Coursera).

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