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I took this class given by Prof. Patrick Winston a while back, 43 or 44 years ago. I liked it so much back then, it served me well in grad school and for many years after. Next week I'm visiting with an AI company on behalf of potential investors.
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You took the class 43-44 years ago? Is that a typo? If not, mind sharing more about your experience?
I took his AI class, 6.258, 45 years ago, in the Spring of 1971. It was probably the first or second time he taught it. He's a great teacher and inspired me to focus on AI. One aspect I remember was that our exams were all take home exams that we had several days to work on. They were great learning experiences.
On just a quick glance, the breadth of the topics covered here is stunning. I also liked this quote from Lecture 23. > A lot of times we ... confuse value with complexity. > And many of the things that were the simplest in this subject are actually the most powerful. > So be careful about confusing simplicity with triviality and thinking that something can't be important unless it's complicated and deeply mathematica…
That's true of most things in CS, I think. Maybe life in general. I'm educated in cybernetics, and our most advanced stuff is hardly ever used. Too costly to implement, tune and test, very few areas where it's truly necessary. PID will work well enough for your application 99 times out of 100. Model Predictive Control is very cool, but it's going to cost you a lot. In places that need advanced regulation but where strict guarantees are not needed a properly trained ANN will beat a more analytical approach and require way less education to pull off.
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Just wondering why do you recommend this particular lecture so highly ?
I'm guessing, because the back half of the lecture has some great life lessons in it.
Really stands out as one of the best lectures I have had the pleasure of watching.
Anyone has lecture 20? - "Lecture 20, which focuses on the AI business, is not available."
Earlier quoted context omitted.
You took the class 43-44 years ago? Is that a typo? If not, mind sharing more about your experience?
I took his AI class, 6.258, 45 years ago, in the Spring of 1971. It was probably the first or second time he taught it. He's a great teacher and inspired me to focus on AI. One aspect I remember was that our exams were all take home exams that we had several days to work on. They were great learning experiences.
Wish you listed a method of contact. I really enjoy having conversations with more experienced hackers.
I have a side project to monitor the homepage of every domain and I want to detect the type of site - ecommerce, blog, forum etc. I've just started on it and ultimately I want to be able to automatically extract data, such as product information from ecommerce sites. Would these videos help here or is there anywhere else that someone would recommend. I know I sounds very naive here... and I am, but there might be som…
1. Get as much data about each site as possible. Try to find data that you think will separate the non-ecommerce sites from the ecommerce sites. This data should be in a table form- think of an Excel spreadsheet, where each row represents a different site.
2. Label the data as belonging to each type of site. This is going to be tedious and take some time. You can also try hiring people via Mechanical Turk to do this.
3. Use [Weka](http://www.cs.waikato.ac.nz/ml/weka/) or [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/wiki) to run some preliminary estimations on the model. Weka and VW are great tools as they come with a lot of the configuration done out of the box, so you won't have to write any code to get started.
Check your results and see how happy you are with them. Weka has a lot of visualization capacities, so you can see how the data that you've collected aligns with the different types of sites.
Now, you can start iterating, which is the key part of any ML project. Consider which aspects of the model you think you can improve on- adding more data, adding more kinds of sites, using a different machine learner.