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Sai

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  • 14 reviews
  • 13 completed
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This course goes through the whole basics of scientific computing, starting with ODE, PDE (wave equation, diffusion equation, etc.), various methods for solving Ax=b, then FFT, Chebychev transform, and finally finite element method. The professor highly recommends getting MATLAB and in the lecture, uses some functions that are not available in Octave. The lecture videos (except for brief opening videos for each week) were from the recordings of his actual course at U of W and about 20-30% are devoted to hands-on demo of MATLAB sessions. (After watching all the lectures, I feel like getting MATLAB is not a bad idea because its IDE is much better than Octave, which essentially has no IDE. It seems Coursera "students" are eligible to purchase a student version.) Professor Kutz is a very humorous guy, making funny remarks all the time, and I have never got bored. His approach to scientific computing is not to devise all the tools by yourself but to take advantage of whatever available to you and concentrate on solving problems. He not only covered the basic theories but also gave practical advice in using various tools and methods. Thanks to him, I did not lose sight of the big picture without being buried in formulae and algorithms. Like other U of W courses, this course does not offer certificate. The assignments were in MATLAB (or Octave), most of which I just skipped.
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This course eventually let you complete a compiler for a language somewhat like Scala. Its target architecture is a simple stack machine but some advanced topics such as register allocation and code optimization were also briefly covered in the lecture. You can choose to not work on the programming assignments but I think you get the most out of this course by doing them. They are either in C++ or in Java and you are supposed to use Amazon's EC2 (sufficient machine hours were offered by Amazon) or download a virtual machine image and work on your local machine. Either way, you need to have some minimum experience of using Linux. What I liked most was that the programming assignments provided a lot of test cases and gave me instant feedback as to what went wrong. What I did not like was also about the programming assignments: The skeleton code seemed pretty old-fashioned and hard to read. I worked with the C++ code but it was from a pre-STL era with hand-written list data structure, not using "const" anywhere, etc. From the discussions in the forum, Java skeleton code seems more or less the same. Other than that, I am pretty satisfied with the course.
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The course starts with simple returns and continuously compounded returns, present values, then autoregressive (AR) and moving average (MA) models, and finally covers portfolio theory and capital asset pricing model (CAPM). Basic probability theory and matrix algebra are also covered on the way but it seemed too lengthy to me (spending almost 2 weeks). There are plain quizzes, quizzes that require some R (or Excel) programming, midterm, and final. Even if you don't know much about R, you can still do the programming assignments in R because sample source files, which are almost giving away the solution, are provided. Some of the R techniques I learned from this course-- bootstrapping and hypothesis testing--seem useful for general data analysis projects as well. Prof. Zivot is (was?) also a practitioner of computational finance and I liked the occasional anecdotes he shared with us. As is often the case with UW courses, there will be no certificate; instead they invite you to enroll in UW's own online course, which you can safely ignore if you are not interested. If you decided to take this course, make sure to read "Viewing the Video Lectures" on the course page.
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You can learn basic AI techniques by building a reasonably behaving Pacman (http://inst.eecs.berkeley.edu/~cs188/pacman/pacman.html) through 3 programming assignments. The course staff did a great job providing a GUI for Pacman where you can watch and check how your program works. It makes learning AI so much fun! You need some familiarity with object-oriented programming in Python because the programming assignments make heavy use of Python classes to decouple specific game definitions and abstract search/learning policies. According to the concluding announcement from the course staff, the sequel of this course, cs188.2x, is planned after the next offering of cs188.1x, which will be in the first part of 2013, is complete. The first offering of cs188.2x will be a closed beta for cs188.1x students.
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This is a graduate level course on a branch of machine learning taught by a co-founder of Coursera. This is one of the toughest course that I have taken so far. Prof. Koller admits that this is a tough one for Stanford students as well. The course page indicates a workload of 8-10 hours per week but expect to spend twice as much. The biggest difficulty I faced with was that the instructions of the programming assignments were poorly written and we had to consult the forum a lot to figure out what we were expected to do. The situation was gradually improved toward the end of the course, though. Please note that I took the first offering of this course in March 2012 and things might be different by now. All programming assignments are in Octave (or Matlab if you can afford it). There were 2 tracks: basic and advanced. Programming assignments were required only for the advanced track. Here is some statistics that I found in the forum: 44,000 students registered, 6,450 students attempted week 1 quiz, 3,070 week 2 quiz, and about 1,100 finished the last programming assignment. Not directly related to this course but you may find Prof. Koller's TED talk interesting: http://www.ted.com/talks/daphne_ koller_what_we_re_learning_from_online_education.html
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This is a very introductory course and maybe best suited for high school students. There was a harsh criticism of this course by a college math teacher (http://www.angrymath.com/2012/09/udacity-statistics-101.html) and Dr. Thrun is going to address some of his concerns in the next version (http://blog.udacity.com/2012/09/sebastian-thrun-statistics-101-will-be.html). MOOC is still in its infancy and I think it is a great attitude to welcome criticism and try to improve based on feedback. Anyway, if you are thinking of taking this course, make sure to do so after the major update.
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This course starts with an introduction to quantum mechanics, develops theory about quantum circuits, and finally explains Shor's algorithm that factors an integer in polynomial time (a threat to RSA cryptography??). The course information page says it does not assume any prior background in quantum mechanics but I think it is an overstatement. Without it, be prepared to do some study on your own. The course does provide a brief introduction but quantum mechanics is so unintuitive that if you understand it solely from this course, I would say you are a genius! In addition, understanding of linear algebra over complex numbers and Euler's formula is necessary. Having said that, this course provides a unique opportunity to learn about quantum computation--I suspect very few universities in the world are offering such course. This course is not for everyone but it shows the great strength of MOOC in matching short supply and distributed demand in education. Here is some course statistics copied from the forum: \- number of people signed up for the course: 26K \- number of people that watched the first lecture: 12K \- number of people who turned in the first assignment: 6K \- number of people who turned in the fourth assignment (midpoint): 3K \- number of people who took the final: 2104 \- number of certificates: 1523 \- number of certificates with distinction: 373
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Great course that deepens our understanding of the world that we are living in. The course textbook is freely downloadable at http://www.earth.illinois.edu/sustain/sustainability_text.html. This course covers broad topics such as population, ecosystems, extinction, climate change, energy, agriculture and water, environmental economics and policy, and ethics. To me, these do look crucial to every global citizen and I think I learned what I expected to learn from this course. I didn't give it 5 stars because of poor video editing: it shows the professor just too long instead of showing the slides.
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I took this course because I wanted to see this legendary figure in computer science live. The course material is pretty much a standard one. If you download the power point, you realize that Prof. Ullman is simply reading the speaker note but nonetheless I appreciated his lectures. The contents get more and more abstract as we move from finite state automata through pushdown automata to Turing machines but Prof. Ullman tried his best to answer students' questions by providing supplementary "Problem Session" videos, which was very nice. My only complaint is about the ugly Python code given in the 2 programming assignments that was hard to read.
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This course is based on Chapters 1 to 3 of the textbook written by the same professors (http://algs4.cs.princeton.edu/home/). Frequent use of animations that simulate algorithms step by step made it so easy to understand how they work. I took this course mainly to learn about Java (I knew C++). All the 5 programming assignments were in Java and Coursera submission page gave you detailed feedback as to which tests passed or failed, etc. I found every assignment well thought out and often dealing with an interesting problem of its own, for example, percolation (using union-find tree) and 8 puzzle (using stack for solving a simple AI search problem). On the other hand, quizzes were very boring where you were asked to simulate the algorithms taught in the lecture by hand (e.g., what does the input array "SDHFIENCPV" look like after 5 swap operations in quicksort?). Since it is Princeton University's policy not to offer any kind of certificate in Coursera (at least up to now), it doesn't matter at all if you skip them, though. I believe Part II, which starts in November, will cover chapter 4 through 6 and am looking forward to it.
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This course covers various technologies behind big data companies such as Google and Yahoo. It puts higher priority on breadth rather than depth. Topics covered in the first 5 units include PageRank, basic machine learning, MapReduce, and storage infrastructures (Google file system, BigTable, etc.). There are 3-4 more units left that will be mostly about machine learning. The course is simultaneously offered to some institutions in India but online students will not get certificate. To me, the biggest downside of this course is that the instructor, who is affiliated with an IT consulting firm, is not willing to share the lecture slides with the students so far, claiming copyright issue or something. That led to some students' voluntarily taking screenshots of the lecture video, compiling them, and sharing them for other students. There was 1 programming assignment about MapReduce (in Python). [Update on Nov. 9, 2012] \- The slides were released a week before the final, thus bumping up the rating a little bit. \- The course staff changed their mind to offer certificates to online students. \- There were 2 more programming assignments, one of which offered an interesting insight about how SQL can be used to compute probabilities in Bayesian networks.
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The lectures and programming assignments give a lot of examples about what kind of algorithms are applicable to solving specific problems. I am not particularly interested in participating in coding competitions. Rather, I wanted to improve my coding skills in Python but found the assignments frustrating in two ways: (1) same execution time limit is imposed whether you submit your code in C++, Java, or Python -- the instructors admit some of the problems are not doable in Python due to this limit, and (2) your code is graded all-or-nothing basis, that is, if you miss a single test case out of dozens, you get zero point. These are simply how grading is done and should hopefully be made more friendly to learners (especially Python users).
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This course covers the basics of numerically solving ordinary and partial differential equations. I like how it picks interesting problems from physics, biology, engineering, and social sciences and shows you how they are formulated as ODE or PDE. The assignments are in Python with some help from the NumPy library (http://www.numpy.org/). If you are interested in more advanced material, I recommend to take a look at Coursera's Scientific Computing (http://coursetalk.org/scientific-computing-uw).
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I have been using Python for 5 years at work and in my personal projects but this course was truly an eye-opener: there are tons of ways that I was unaware of to use this powerful programming language to attack problems. It was a very pleasant experience to watch how elegantly Dr. Norvig uses Python to solve games and other computer science problems. I am sure that even more seasoned Python programmers than I would share this feeling with me after taking this course. If you are interested, you can watch Dr. Norvig's thoughts on MOOC in his TED talk at http://www.ted.com/talks/peter_norvig_the_100_000_student_classroom.html.