Note:
Registration for the recorded version of the course
will open mid June
Caltech Professor Yaser Abu-Mostafa covers the basic theory of machine learning in this distance learning course. The 18 recorded lectures are here and the rest of the course material is linked from that page, or is here. Each lecture recording is an hour of lecture, followed by a half hour of recorded question & answer.
In order to do the homework, you will need to write some programs - perhaps Python is best. I had Mathematica too, which I used for visualization. You will need a quadratic programming package, which with Mathematica, costs $$, but there is freeware for Python. Most of the homework is useful.
There is a book, too, "Learning from Data: A Short Course", by Yaser Abu-Mostafa and others; the course covers more than is in the book. The book adds some depth to the areas that it covers, and if you're going to spend time on the course, having the book is probably worthwhile.
Professor Abu-Mostafa is a very good lecturer; and overall I rate the course highly. I met my objectives, which was to get a view of the foundations. Supposedly "data science" is an emerging disciple and machine learning is one of the weapons in the data scientist's arsenal. Now I'm a bit better prepared to evaluate prospective data scientists.
The most "aha!" moment in the course was with Support Vector Machines; and the most vague concept in the course was that of deterministic noise.
The main drawback in distance learning is the relative isolation; however distance learning is a way the problem of the very high cost of higher education might be addressed. Now anyone with a reasonable internet connection can take a fairly substantial course from Caltech.
I took the course "live", there were two lectures a week through April and May, and I just submitted my answers to the final exam. I suppose henceforth one could take it self-paced.
(Added later): The above probably doesn't sound like an ringing endorsement for this course. That is more due to my nature than to the course. But if it is a subject that interests you I strongly recommend it.
Registration for the recorded version of the course
will open mid June
Caltech Professor Yaser Abu-Mostafa covers the basic theory of machine learning in this distance learning course. The 18 recorded lectures are here and the rest of the course material is linked from that page, or is here. Each lecture recording is an hour of lecture, followed by a half hour of recorded question & answer.
In order to do the homework, you will need to write some programs - perhaps Python is best. I had Mathematica too, which I used for visualization. You will need a quadratic programming package, which with Mathematica, costs $$, but there is freeware for Python. Most of the homework is useful.
There is a book, too, "Learning from Data: A Short Course", by Yaser Abu-Mostafa and others; the course covers more than is in the book. The book adds some depth to the areas that it covers, and if you're going to spend time on the course, having the book is probably worthwhile.
Professor Abu-Mostafa is a very good lecturer; and overall I rate the course highly. I met my objectives, which was to get a view of the foundations. Supposedly "data science" is an emerging disciple and machine learning is one of the weapons in the data scientist's arsenal. Now I'm a bit better prepared to evaluate prospective data scientists.
The most "aha!" moment in the course was with Support Vector Machines; and the most vague concept in the course was that of deterministic noise.
The main drawback in distance learning is the relative isolation; however distance learning is a way the problem of the very high cost of higher education might be addressed. Now anyone with a reasonable internet connection can take a fairly substantial course from Caltech.
I took the course "live", there were two lectures a week through April and May, and I just submitted my answers to the final exam. I suppose henceforth one could take it self-paced.
(Added later): The above probably doesn't sound like an ringing endorsement for this course. That is more due to my nature than to the course. But if it is a subject that interests you I strongly recommend it.
