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Equanimous Creativity

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  • 33 reviews
  • 32 completed
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I took this course because it is a recommended background for the course "Principles of Reactive Programming". In this course Martin Odersky show off the functional elements of Scala. If you want to learn functional programming then " Introduction to systematic program design - part 1" is much better. If you want to lean Scala  the this course is not extensive enough. If you are wondering how the functional paradigm is expressed in Scala then this course is the answer. The programming assignment was quiet easy when you have the systematic program design course. All in All I didn't learn much in this course something got clarified and I got to see another language. The course is okay made and a more junior programmer might find it useful.
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I took Information Theory because it is super important when working data science and AI. Unfortunately this course focus entirely on coding theory but I still learned something I can use in AI. I don't understand why this is the only MOOC I can find in Information Theory. The course itself is not a good MOOC. The lecture videos is just the proves from the book read out. All the homework is made as peer assignment instead of quizzes. Which means that you get feedback on your homework 2 weeks after you made it. In conclusion a super important subject but a very poor MOOC implementation.
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I would wish a course like this had been available when I learn myself programming 25 years ago. While they say it is a introductory programming course those who had never programmed before seems to have a hard time with this course. But If you have already made some programs on you own, then this course is perfect. I took this course for the systematic programming part, I wanted to find out if there was any idea I could use for automatic programming. While nothing directly applicable for that purpose, I learned how to program almost without thinking :-) They teach you a set of recipes you follow from information to data structures to function temples to unit tests to concrete functions. It feels like the programs almost write themselves. A nice course which made me a better programmer even through the first couple of weeks was very easy and a little boring.
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This course have the potential to be a great course. To get the most out of this course you should have some background knowledge in programming, machine learning, information theory, electrical circuits, bioelectricity or else you might get overwhelmed by the complex math used in the course. But if that is not a problem for you then it is a very inspirational course. I join to get new ideas for brain inspired AI techniques, and I got a couple of new ones while learning how the brain process information. The biggest problem holding the course back from being a great course is that they do not provide the lecture slides for download. Citing copyright problems as the reason. I find that access to the slides is the most important thing of a MOOC after the lecture videos. Also the course only had the 2 lectures working on it not a team like most MOOCs, it showed but those 2 did a fantastic job. I think that next it runs most of the problem will be solved and if you have the math skills and the interest I will recommend it.
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I took this class first time it was offered at coursera. I think this class is the hardest to review, because the quality varies from wasting our time with detailed videos on simple things over mindblowing insights into the nature of the subject to advance stuff without any details.
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I liked this course very much. The homework is very challenging and interesting. When you pass this course you feel confident that you will be able to write a bioelectic simulator if you have to. I took this course the first time it was offered and the was something that could be better but all in all an excellent course.
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This is an update of the 2013 course Semantic Web Technologies. Normally, I don't repeat courses but the 2013 course was one of the best MOOCs I have ever taken, and knowledge engineering I find very interesting. It was only a small update from the 2013 course which was a little disappointing. If you dream about building systems like IBM Watson this is where you start. You will not learn everything to build such a system, but what you learn in the course is the foundation. It will also teach you how to make your websites play nice with the semantic search engines of the future.
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This course have good lectures about PID-control and Kalman-filters for flying robots. The material on SLAM is very thin, so don't join the course to learn about SLAM.
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Just started. Experiments is the central element in the scientific method. Learning how to get the most out of you experiments sounds promising.
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I took this course first time it was offered as a MOOC. From the presentation video I got the impression that Dories Buffet would take part in lecturing the course.  But all we got was an interview with Dories and Walter. The first big assignment for the course is nominating organization for donations, but you could only nominate local organizations in the USA, so as a foreign student you was left out, not a good start. The  method taught for evaluating organization was not very complex and the course didn't have much material. The assignments with evaluating the organization did take a lot of time. As discussion forum they used google plus which is not really constructed as a discussion forum,  very hard to get a good discussion going. I was also missing discussion about new crowd-funding sites like kickstarter, indiegogo and flattr in the lectures. I posted a message about flattr on the forum but after some days it was gone again, very mysterious. I hope the team behind the course get the problems solved and make a great course for future students.
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Do you want to know how a compiler works? or do you want to make your own general purpose programming language ? If the answer is yes to the first question and no to the second question then this course is for you. While it gives fundamental of implementing a compiler it feels like the course don't teach you what you need to make  a compiler for a modern programming language. It feels like we are only taught enough to implement the COOL language and the COOL language itself feels a little dated. Also the deadlines for midterm and final exam collide with deadlines for the programming assignments, making following the line with the programming assignments almost impossible for people which is not full-time students. Be aware that have around an equal amount of time to each programming assignments but the workload is more like 1 :1:3 :5. Get started early on the last 2.
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This is one of the best MOOCs I have taken. OpenHPI platform was well thought out and easy to use. The material was interesting and close to current research.
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This is one of the best and most time consuming courses I have taken. At this courses there is programming assignment each week if you have no experience in programming take "Introduction to Systematic Program Design" before attempting this one. My only which was that biological part of the course was bigger.
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I took this course because I wanted to learn about the theoretical side of Computer science. The lectures can be very abstract at times but the homework help to give you a good understanding of what he was talking about. I borrowed the book at the library while I took the class which was a great help.
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I took this course first time it was covered on coursera. This course was a little disappointing for me. I had expected this to be an advance AI course and when ask for a syllabus they refereed to the online notes, which showed both basic and advance material. But as the course progressed we only got on average 15 minute of lecture per week, and for the first 8 weeks only the basic material like Minimax trees, Alpha-Beta pruning Heuristic and Monte Carlo search was covered. Materiel which is expected to be covered in an introductory AI course, but in an advance course it should be in the review material in the first few weeks. Only in the last two weeks was a few of the advance topic covered and not in great detail. Along the way you was recommended to write your own game player which you could enter in a competition at the end. I think the subject is interesting but more focus should be on the advance stuff and much lesser on basic stuff which students with an interests in AI have probably seen before.
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I was a little disappointed with this course, It might be that I had high expeditions because I heard a lot of good about it. Don't misunderstand me this course is a very good first course on machine learning. The lectures, review questions and programming assignments is very good and easy. But the material have been simplified and details is missing compared to the Stanford lectures. A subject I find very useful when working with learning algorithm's is information theory but it was not mentioned with one word. A good course if you have never heard about linear/logistic regressions, neural networks and clustering but If you have seen it before there is not much inside and details to get in this course.
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I think "systematic debugging" could have been a more precise title for this course. Because it doesn't cover how to write unit tests or using a debugger. It teaches how to write code which can help isolate the bugs in your software. At Udacity you can start the course at any time and there is no deadline, which is nice. But it also means that I haven't finished exam because I been busy with other courses.
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I was a little disappointed about this course. I would say that the course is okay if you know noting about the subject, but if you know a little there is not much information to get. If you are very interested in the subject I will suggest you read " Tribal Leadership: Leveraging Natural Groups to Build a Thriving Organization" by Dave Logan, John King and Halee Fischer-Wright. Which gives a framework to think about the challenges. Something I think was missing from the course. The good point i can say about the course is that the professor was very enthusiastic, the slides was okay and the professor was very active on the forum. The down side was that the course was not prepared well for running online. In the video lecture the camera was mainly on the professor and not on the slides which made is problematic to watch them on double speed. The was no quizzes apart from the ones in the videos, but instead we was ask to post our answers in the forum, robing us from instance feedback.
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Wow, this is a great course. The course is about the type of software it takes to make modern processors (not quantum processes). From logic to layout, while I don't plan to build my own ICs i join to learn some cool algorithms. Around the first half is about computational boolean algebra which is also very useful in AI (which I try to learn). The next half is about technology mapping, gate placing, wire routing and timing analysis. Areas with many hard problems and interesting heuristics. Instead of playing with toy problems like other courses you get to work on industry level problems. NICE ;-) If your following the mastery track, you will do 4 programming assignments in a language of your choosing. The programming assignments is fairly easy but also very educational. I liked them a lot. Only point of criticism is that you only have 1 try on the homework, which I feel is not optimal for learning.
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If you have a stock portfolio and you want to take the next step and start to trade more exotic contracts: options, futures e.t.c. Then this course is a good start to get a grip on how to price those exotic contracts. Very cool stuff. Most of the assignments uses excel, because excel isn't available for my Linux systems I programed most of my assignments, until I discovered that LibreOffice have a non-linear solver as a addon. Maybe next time they should base the assignments on LibreOffice which is non-proprietary and runs on more platforms. The course is excellent converted to the online form, the videos is small segments on each concepts. You have 100 tries on the assignments, so you can submit until you eliminated all your mistakes. All in all an excellent course.
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Nice course on automatic planing, I wish that the course was longer and more detailed. They could have expended on all the variants of A* (D*, INA*, GAA*, LPA*,SMA*,Theta*) and covered planing with uncertainties in detail.
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A nice introduction to microeconomics, but notice when they write scientists they mean science students. The home work take some time and you only have one try, so you can't correct your calculation and try again. This take away some of the fun of online courses, this effect accumulate and in the end I really need to motivate myself to complete this course.
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Nice, introduction to control theory. I leaned a lot but had hoped there would be something about building electronic motor controllers for robots. But all in all an excellent course. One small point of critic is that the optional programming problems is in Matlab (proprietary software) and can't run on octave (open source software).
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I took the course first time it was offered. I think it is a very interesting subject and was very happy that someone would teach the subject. The first week was fine he picked up where "Introduction to Computational Finance and Financial Econometrics" ended. Nice. The problem of the first assignment was also nice, next we had to use linux some python library. (A course which introduce students to open source software, very nice). All in all a very good start. But then the course started to fall apart. Because the professor hadn't prepared the material in advance. He though he could wing it and make the material as the courses went along. The rest of the lectures was very low on contents.
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Nice course if you have or want a stock portfolio. Teach you the fundamentals on how to wight return and risk. If you know about probability and matrix calculations the 3 first weeks is very boring but it get much better.
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A very academic overview on Sustainability e.g. how is the earth doing on water,energy,food and population. I was missing a personal perspective e.g. if I become vegetarian /installed solar panels on my house or simmilar what impact would it have.
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I took the course first time it was offered. Nice introduction to gamification covers all the fundamentals, only time I got a little frustrated was that he didn't cover the details of one example, a system called idea street.
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I took the course when it was first offered and I have to say that it was one of the worst MOOCs I have completed. First the platform wasn't ready so we had to follow the professors blog. Also the professor had the idea that we should learn most from group work, so the video lecture's was very sketchy. The problem with group work and MOOCs is that many who sign up don't complete the course. So, in my team with around 20 person only 2 was following it at the end. Also the professor seams to follow the what I call the lottery approach to entrepreneurship. 1) Get lucky and get a good idea. 2) Get lucky and find someone to fund you idea. Instead of teaching students about passive income and how to grow it.
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I took this course when it was first offered at MITx. In this course you lean to make fundamental calculation on resister, capacitors, coils, diodes, transistors and logic gates. If you looking for something more practical like learning to build a motor control for a simple robot this course is not it. The course was very well organised at very good platform, it was a pleasure to follow.
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If your interested in Natural Language Precessing this is a good place to start. The workload is high so be warned. I was a little disappointed at the end, the syllabus said that they would cover question answering but they didn't show us a full semantic answering system but merely how to rip possible answers patterns out of the text.
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This course is very hard and very useful. Knowing how to build Probabilistic Graphical Models is fundemental if you need to build models from data.
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This was my first MOOC and a very good one, nice introduction to the field of Artificial Intelligence.
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I think this is the course I learned the most compare to the workload. It is simply pact with interesting models and insights. I always recommend this course as a first MOOC.