Intro to Artificial Intelligence....

naturalist.atheist · #1 09-11-2011, 04:24 PM

I just signed up for this class. It's been decades since my last class so I hope I can cut it. It is open to all takers and they will grade your homework and exams. However it is not good for Stanford academic credit unless you are a registered Stanford student. But they will email you a certificate with your class standing.

ai-class.com - Introduction to Artificial Intelligence - Fall 2011

naturalist.atheist · #2 10-09-2011, 12:45 AM

Class starts on the tenth. It looks like they moved the site to

ml-class.org

and it is still open for enrollment. The first set of lectures is available online with quizzes. For some reason I am having a heck of time with the first quiz but ace all the others. I can to the math standing on my head but the AI concepts still elude me.

I've posted a request for study group/project partners on the class forum.

I wonder if they will post statistics on class participation as the class proceeds through the material. It looks like they have close to 100,00 students enrolled.

The class also has a twitter feed:@ml_class

I have a sneaking suspicion that this class will be historic and one can only wonder what will come of so many people learning the basics of programming machine intelligence.

Machine Learning: About the class - YouTube

Dragar · #3 10-09-2011, 12:48 AM

You've done much coding na?

naturalist.atheist · #4 10-09-2011, 01:23 AM

Quote:

Originally Posted by Dragar

You've done much coding na?

At least 5 million lines over 40 years worth. Wrote my first program on an IBM 370 in 360 assembler in the IBM Boca plant in 1970 when I was 17. But from what I understand there will be more math than coding in this class. Apparently an "algorithm" in machine intelligence is more like a formula which is then "programmed" in MathLab or GNU Octave. If things go well in the class I my try to do some of the assignments in F#.

Dragar · #5 10-09-2011, 01:36 AM

You should be fine then! You've done plenty enough I imagine (way more than me!).

Ammembly code is weird. I don't know how anyone codes in that. Part of the course I'm teaching right now involves deciphering the assembly code compared to the Fortran code, and it was pretty esoteric!

Oh, and report back with your findings on this course.

naturalist.atheist · #6 10-09-2011, 01:42 AM

Will do. Wish me luck.

ceptimus · #7 10-09-2011, 04:42 PM

Good luck!

I would be interested in signing up. But I know I don't have the free time to do the necessary work

I look forward to your reports!

naturalist.atheist · #8 10-13-2011, 01:45 AM

There is a meetup for a study group in Tallahassee...

Stanford Machine Learning Course - Tallahassee (Tallahassee, FL) - Meetup

Iacchus · #9 10-13-2011, 05:59 AM

Quote:

Originally Posted by naturalist.atheist

Quote:

Originally Posted by Dragar

You've done much coding na?

At least 5 million lines over 40 years worth. Wrote my first program on an IBM 370 in 360 assembler in the IBM Boca plant in 1970 when I was 17.

Hey, same here! Except it was at work in Sunnyvale, California, and it was about 10 years later. But no, I haven't written much code since then, until recently, when working on web pages and writing forex trading advisors.

naturalist.atheist · #10 10-14-2011, 12:58 AM

Quote:

Originally Posted by Iacchus

Quote:

Originally Posted by naturalist.atheist

Quote:

Originally Posted by Dragar

You've done much coding na?

At least 5 million lines over 40 years worth. Wrote my first program on an IBM 370 in 360 assembler in the IBM Boca plant in 1970 when I was 17.

Hey, same here! Except it was at work in Sunnyvale, California, and it was about 10 years later. But no, I haven't written much code since then, until recently, when working on web pages and writing forex trading advisors.

Hardly the same thing. The 370 was brand new back then, the PC wasn't invented. Networking was just beginning to be developed as a military project and it would be decades before the idea of HTML would occur to anybody. The 370's were massive machines that could cost millions of dollars back in the 70s, when a million dollars was serious scratch. They were not something that you would let just anybody near, let alone a 17 year old. And let alone allow the 17 year old free reign of an entire development floor with dozens of mainframes of all kinds. It's flat out amazing when I think back on it.

It was a very different world.

Iacchus · #11 10-14-2011, 02:39 AM

It was a VM 370, although I guess it had been refurbished and they got some sort of deal through IBM. But yeah, the system was massive, and took up a good 30' x 30' room (or more?) with the peripherals and all. It seemed like it had a lot of downtime though, because it was an older machine.

naturalist.atheist · #12 10-24-2011, 04:52 AM

A few weeks into the machine learning course and so far so good. I'm learning how to use GNU Octave, which is a kind of freeware version of MathLab. This is a very powerful tool for numerical analysis of all kinds. So far machine learning algorithms look like various wrinkles on linear regression.

naturalist.atheist · #13 11-05-2011, 03:43 AM

Who would have thunk it! Character recognition is an application of linear regression.

naturalist.atheist · #14 11-06-2011, 03:53 AM

I'm working on the third assignment, and getting a 95.1% accuracy recognizing handwritten digits 0-9 using a simple linear regression technique called Logistic Regression.

All digit images are 20x20 8 bit gray scale images. Each image is converted into a 400x1 vector.

The idea is that for detecting a particular digit like say "2" there is an equation of the form:

f(theta * X)

where theta and X are vectors which in this case would be 401 values (20x20 + 1) in length.

In the case of logistic regression the output desired is something that stays between 0 and 1 and mostly 0 or 1 with intermediate values when transitioning from 0 -> 1.

In this case the sigmoid function is used:

f(z) = 1/(1 + e^z)

The "hypothesis" for the problem would then be stated as:

h(theta, X) = (1/(1 + e^(theta *X))

The trick is finding the magic theta coefficients. This is where the "training" comes in.

A set of 5000 images is supplied with the value each image should have. This is called the training set. The value of each image would be in a 5000 x 1 vector called y. If the model is perfect and the thetas are perfect then for every image in the set:

h(theta, X) - y = 0

Given say 5000 images they are converted into a 5000 x 401 matrix. Each row in the matrix corresponds to a particular image and each column of that row corresponds to a pixel in that image. The extra 1 column is set to 1. This is for the bias coefficient of theta (theta(0)).

However in order to use the "training set" to determine the thetas you need to come up with a "cost function". This should produce a "convex" function with a single global minimum. Such a function will then be amenable to numerical techniques for finding the global minimum such as gradient descent, newtons method and so forth.

The cost function in the case of logistic regression is a somewhat complicated expression:

cost function(theta) = -y * log(h(theta, X)) - (1-y)log(1-h(theta, X))

The gradient of this cost function has the somewhat simpler form:

grad = (h(theta, X) - y) * X

Using one of several possible minimization techniques the "training" set is iteratively used in the gradient of the cost function for every sample in the training set. This results in a set of thetas that can then be used in the hypothesis to take a new 20x20 digit image and determine if it is the digit trained against.

Needless to say I've left out a lot but by using GNU Octave there is very little actual branching and looping involved. It's mostly vector equations and minimization functions.

naturalist.atheist · #15 11-06-2011, 06:47 AM

This is a demonstration of the kind of results you can get with a more sophisticated neural network model:

naturalist.atheist · #16 11-06-2011, 07:36 AM

I just finished the three layer neural network assignment. This stuff is very, very cool. To see all the different handwritten numbers just magically get recognized is just way too cool. I could not even begin to think how I would perform such a task using a traditional programming approach. I feel as excited as I did when I wrote my first "Hello World" program in C thirty five years ago.

naturalist.atheist · #17 11-19-2011, 05:08 PM

I'm halfway through the course and so far so good. The instructor, Andrew Ng, is doing a fantastic job. If this represents the level of instruction at Stanford then it is no surprise that it is such a well regarded institution. His approach is very practical so I am very hopeful that I will be able to apply what I've learned.

Dragar · #18 11-19-2011, 05:25 PM

One of the other PhD students in my year uses neutral networks for photometric redshift fitting of distant galaxies. All this talks of coefficients and training sets is ringing bells now.

She did say they don't use them very much for AI these days though, as they don't seem to work in the way they were hoped they would.

SR71 · #19 11-19-2011, 05:29 PM

Maybe someone can get adobe to fix PDF so it can sort typewritten letters and numbers. Fun is finding out you didn't get your critical part because it was stock number 386B, not 3868.

naturalist.atheist · #20 11-19-2011, 05:38 PM

Dragar, that doesn't correlate with the instructor's take on the success of such techniques. He seems to think that they've been very successful. Especially now that we are well along Moore's curve. He does seem to think that many people are having trouble applying these techniques because they don't know what he is currently teaching us. Certainly, if in principle a brain neuron is as simple as he models it in his class, then theoretically by recreating a similar or equivalent computer neural network, it should be possible to reproduce complex human behavior in a machine. Of course the complexity of human networks is several orders of magnitude greater than what can be accomplished now with even the largest of computers and the goals of machine learning are much less ambitions. Rather than reproduce all the abilities of a functioning human brain they are simply trying to reproduce small subsets of behavior.

A neural network in principle is simple. But training it appears to be the tricky part. It's certainly been difficult training the neural network between my ears.

naturalist.atheist · #21 11-19-2011, 05:42 PM

Quote:

Originally Posted by SR71

Maybe someone can get adobe to fix PDF so it can sort typewritten letters and numbers. Fun is finding out you didn't get your critical part because it was stock number 386B, not 3868.

That error was probably introduced by the neural network pulling the item from the shelf.

SR71 · #22 11-19-2011, 05:43 PM

Did the course give a blurb definition of AI?

naturalist.atheist · #23 11-19-2011, 05:50 PM

I was taking the AI course but didn't find it very interesting, so I've dropped out. The machine learning class defines a machine learning algorithm as one that gives a computer the ability to learn how to perform a task without being explicitly programmed.

Dragar · #24 11-19-2011, 08:48 PM

Quote:

Originally Posted by naturalist.atheist

Dragar, that doesn't correlate with the instructor's take on the success of such techniques. He seems to think that they've been very successful. Especially now that we are well along Moore's curve. He does seem to think that many people are having trouble applying these techniques because they don't know what he is currently teaching us. Certainly, if in principle a brain neuron is as simple as he models it in his class, then theoretically by recreating a similar or equivalent computer neural network, it should be possible to reproduce complex human behavior in a machine. Of course the complexity of human networks is several orders of magnitude greater than what can be accomplished now with even the largest of computers and the goals of machine learning are much less ambitions. Rather than reproduce all the abilities of a functioning human brain they are simply trying to reproduce small subsets of behavior.

A neural network in principle is simple. But training it appears to be the tricky part. It's certainly been difficult training the neural network between my ears.

Odd. Perhaps its the distinction between machine learning (where I've only ever heard success about them) and AI. Perhaps we are using the word AI in different ways.

You're closer to the source, anyway, so it's interesting you're saying they've been very successful.

naturalist.atheist · #25 11-19-2011, 11:55 PM

Quote:

Originally Posted by Dragar

Quote:

Originally Posted by naturalist.atheist

Dragar, that doesn't correlate with the instructor's take on the success of such techniques. He seems to think that they've been very successful. Especially now that we are well along Moore's curve. He does seem to think that many people are having trouble applying these techniques because they don't know what he is currently teaching us. Certainly, if in principle a brain neuron is as simple as he models it in his class, then theoretically by recreating a similar or equivalent computer neural network, it should be possible to reproduce complex human behavior in a machine. Of course the complexity of human networks is several orders of magnitude greater than what can be accomplished now with even the largest of computers and the goals of machine learning are much less ambitions. Rather than reproduce all the abilities of a functioning human brain they are simply trying to reproduce small subsets of behavior.

A neural network in principle is simple. But training it appears to be the tricky part. It's certainly been difficult training the neural network between my ears.

Odd. Perhaps its the distinction between machine learning (where I've only ever heard success about them) and AI. Perhaps we are using the word AI in different ways.

You're closer to the source, anyway, so it's interesting you're saying they've been very successful.

I'm not sure if this is AI but I would consider this progress in AI

Jeopardy! IBM Watson Day 3 (Feb 16, 2011) Part...

&feature=related
And there is autonomous driving which is considered machine learning

Google Self-Driving Car - Part 1: Outside -...

&feature=related

Google autonomous car - YouTube

Now this one is very interesting. It is also an application of machine learning. What makes it interesting is that it can make that helicopter do things that a human could never do. And there was not one line of code written that knew anything about aerodynamics. It would be the way a helicopter bird could fly if there were such a thing. Andrew Ng was involved in this project.

Stanford Autonomous Helicopter - Airshow #1 ...

And then there are the obvious military applications:

http://www.youtube.com/watch?NR=1&v=yDVLUiJfpPw