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Ethan Berl

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  • 4 reviews
  • 4 completed
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Wow. I am so impressed by this course. Of the 11 Coursera classes and 1 edX course which I actively participated in so far (most of which were in Machine Learning, Artificial Intelligence, NLP, etc.), I think this one might be my favorite. Unlike some of the other courses from Stanford or Berkeley which cover similar subjects (and are great as well), this course takes a much more practical/applied approach and examines the technologies used to actually perform these techniques as well as give a great overview of current methods. Nowhere else would I learn as much about state of the art techniques like HTM. Nowhere else would I get to play around with data on web scale using techniques like Map-Reduce. Nowhere else could I get as complete of a picture of the current methods for having computers simulate intelligence, make deductions, run logic based systems, use collaborative filtering, perform pattern recognition, etc. To get the same value as this course gave, at the least I would have to take courses on Information Retrieval, Natural Language Processing, Machine Learning, Artificial Intelligence and then do the hard work of tying the different subjects together. Obviously I think that this class was amazing. The quizzes were good tests of if you had understood the content of the lectures. The homeworks were a little sporadic and seemed slightly poorly planned but they were challenging and most importantly, they actually taught you something rather than merely testing if you could parrot back what the lectures had just taught you. I had a feeling of accomplishment when I finally got my tf-idf calculation running in map-reduce format or when I implemented a Bayesian-net to play doctor at diagnosing disease. In terms of practical applications, this course blows every other course out of the water.
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What an amazing and fun course. Kevin Werbach ties in psychology and business, gaming and social good to give a picture of current uses of gamification and provide insights into how one might design a gamification system of their own. There are many frameworks which for me were intuitive but at the same time helped me to organize and solidify my thoughts on the subject. There is a lot of information in the class but it is not too technical (in comparison to the programming and math classes I normally do). The most interesting parts in my opinion are when professor Werbach interviews someone who works in the field. I only had one bad experience, but it wasn't the fault of the course. There was a peer graded question which asked us to NOT design but merely explain why a given problem was right for gamification to be applied to it and when I did this, several of the other students grading misunderstood and marked off points with the only comment being that I did not design a system. The question had clearly stated that we should not design a system but they expected that I should for some reason. Besides this misunderstanding which was in no way the fault of the course or Prof. Werbach, everything was very smooth and insightful.
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This course is very good and well planned. Andrew Ng explains the material very well (albeit a bit slowly) and the content is extremely useful. The absolute most useful part of the course is that he focuses on how to tell when the algorithms are working and how to tell when something is going wrong. Over/under fitting, regularization, learning curves, precision vs. recall, etc. give a real insight into the subject rather than just handing the student a toolbox of algorithms which could be misused. The actual algorithms cover all the established techniques very well. The one big complaint I had with this course was that the homeworks and quizzes were too easy. You were able to fill in the few lines of Octave code without really having to understand the algorithm completely, which to me is a fatal flaw and defeats the purpose of the homework. I was able to get full points on everything but I know that I would not be able to implement SVM in another language after the course -- even though I do have a reasonable overview understanding of what the algorithm achieves. Because of this hole, I can't give the course a perfect rating but other than this, the video lectures were excellent and the material is so useful I often refer back to it even though the course ended several months ago.
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This course is an excellent introduction to anyone who is interested in what quantum computing really is and has a slightly technical background. I took a course which covered the same material in person at Princeton a year earlier and there were some things I thought this course made much clearer (quantum FFT for example). If you take this course you will learn a lot about functional aspects of the nature of quantum systems and quantum mechanics in general, without having to get your hands too dirty with wave equations. You will get a sense of how quantum algorithms work and where the promise of their exponential speed ups stem from as well as understanding the most significant algorithms discovered so far. I thought that the homeworks and tests were actually very good tests of skill which were right on the border of challenging and possible so that with focus you really learned something. There were two things that this course could do better but neither was a very big detractor. Vazirani assumed a certain background knowledge of math and certain conventions which I and a lot of others just didn't have -- nothing conceptually difficult, just facts which I didn't know. This could have easily been fixed with a 10 minute math review video at the start of the course but instead I had to dig around the forums and find out that the problems relied on you knowing some facts about a convention in complex fields or certain differential equations, etc. It wasn't a big set back, but it was annoying that I had to spend a few hours teaching myself when it could have been a ten minute video. The other issue was that we never really talked very much about how quantum computers could actually be implemented. The whole course we just assumed that if it became possible for quantum circuits to be built that these are the properties and algorithms which could be run on them. I think I would have very much enjoyed a week of lectures on the most promising current approaches and more explanation of the problems which have prevented them from being a reality so far. At any rate, my rating reflects the material which was there not the material which could have been there and this course was excellent.