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Discrete Optimization University of Melbourne at Coursera

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Discrete Optimization University of Melbourne at Coursera
Discrete Optimization University of Melbourne at Coursera
English | .MP4 | h264, yuv420p, 1024?768, 464 kb/s, 1000.00 fp | aac, 44100 Hz, 127 kb/s | 4.24 GB
Genre: E-Learning

This one was a really though course. Although it was though I really liked it.

First of all: the video lectures were not the same, which I was used to. The introduction of the course made me really laugh. Prof Hentenryck made the firs lecture interesting and it was a good motivation to dive into the course.

The lectures are available from the start, so if you have time and eventually prior knowledge you can just jump-start into the middle of learning. However there is a recommendation how one should watch the lecture and advance on the topics of dynamic programming, constraint programming, local search and mixed integer programming. And besides this, there were some new lectures introduced during the course to help the students understanding the concepts of greedy algorithms and to help them solve the homework.

After your result of the problem is sent to the server it is evaluated. There are some margins defined and you get appropriate scores after your results:

3 points for a feasible solution
7 points for basic optimization
10 points for a very good optimization

Because these problems are NP-hard there are no correct solutions (only feasible naturally), so you do not have to have the ultimate problem solver algorithm backing you up because 7 points are easily reached, 10 require a bit more effort.

These were the weekly assignments of the offering I took:

Knapsack problem
Graph coloring
Traveling Salesman Problem
Warehouse Location Problem
Vehicle Routing Problem

Such puzzles are:

N-Queens Problem
All Interval Series
Magic Series
Magic Square

And for all problems there is a leader board too where you can compete with the other students: who has the best solution for a particular problem.

So if you are interested in optimization and want to learn something new and do some coding yourself, give it a try. Its fun every week and you can tweak your algorithms every time a bit to get a better solution.

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