I’m about to take my e-learning to a new level - I enrolled in all three of the Stanford online classes: Artifical Intelligence, Machine Learning and Introduction to Databases. It’s free, it’s serious – with tests and all, and it’s going to take a lot of time. How am I going to make it, now that I have an 8 hour work day? I’ll just have to find a way to make it work, plus find extra time for my first Open Source contributions and Github experiences, and the blog and web app security updates etc. I’m just hoping that this thing a friend of mine said once is true: the more you do things the more things you manage to do. Activity furthers activity.
So, the classes are almost starting, and I’m already starting to watch the introductory videos. I’m learning about basics kinds of machine learning (supervised and non, classification and regression, clustering and the cocktail party algorithm, …), passing the first quizzes and so on. It’s inspiring, fun and motivating, but I can already tell it’s not going to be easy.
I’m going to need to become VERY good at planning and time management to pull this off. I’ve already calculated how many hours a week do I have for my job-unrelated computer projects, and divided them up between different projects. Then I decided the next doable steps for each project. All I got to do next is do it. All. One little next doable step at a time.
Now, about the online classes. Artificial Intelligence is an interesting field to get to know more about. For one thing, I want to go beyond the usual misconceptions and stereotypes about it. I’m also interested into anything cognitive, and am definitely in the process of learning more about math and algorithms. Mostly I am passionate about learning, e-learning, learning about learning, metacognition and knowledge representation, and hope this course can help me take first step in that direction. As this Read Write Web article explains, the AI course “will be taught by Sebastian Thrun and Peter Norvig“, and
“Thrun is a Research Professor of Computer Science at Stanford, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. He’s probably best known as the lead developer of Google’s self-driving car, as well developing autonomous car projects at DARPA.”
And unlike what it says in the article, the enrollments are still open, at least until the 10th of October. AI and ML courses let you choose between an advanced and a basic track, and you can switch between them later on.
Machine Learning came up when scientists and programmers discovered that, in order to teach computer certain skills such as driving a helicopter, the best way was to let the machine teach itself. Arthur Samuel came up with a definition in 1959: Machine Learning is the field of study that gives computers the ability to learn without being explicitely programmed. Arthur Samuel also thought a computer to teach itself checkers until it could play it better then he played it himself. Obviously the only way to do that is to teach the machine without telling it explicitely what to do. About the course Professor, from the course website:
“Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus.”
And the Introduction to Databases one? I’ve been trying to take a more careful and beyond-the-obvious look at whole db thing, reading SQL Antipatterns and all. This is a chance to do it seriously. And the Professor sounds interesting. The course website informs us that:
“Professor Jennifer Widom is the Fletcher Jones Professor and Chair of the Computer Science Department at Stanford University. She received her Bachelors degree from the Indiana University School of Music in 1982 and her Computer Science Ph.D. from Cornell University in 1987. She was a Research Staff Member at the IBM Almaden Research Center before joining the Stanford faculty in 1993. Her research interests span many aspects of nontraditional data management. She is an ACM Fellow and a member of the National Academy of Engineering and the American Academy of Arts & Sciences; she received the ACM SIGMOD Edgar F. Codd Innovations Award in 2007 and was a Guggenheim Fellow in 2000; she has served on a variety of program committees, advisory boards, and editorial boards. “
I always find it encouraging to learn that I’m not the only person in IT with a humanistic degree, and that will have its own post some day.