Discrete Optimization

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9/10 stars
based on  34 reviews
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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4724 reviews

Course Description

Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming. Optimization technology is ubiquitous in our society. It schedules planes and their crews, coordinates the production of steel, and organizes the transportation of iron ore from the mines to the ports. Optimization clears the day-ahead and real-time markets to deliver electricity to millions of people. It organizes kidney exchanges and cancer treatments and helps scientists understand the fundamental fabric of life, control complex chemical reactions, and design drugs that may benefit billions of individuals. This class is an introduction to discrete optimization and exposes students to some of the most fundamental concepts and algorithms in the field. It covers constraint programming, local search, and mixed-i... Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming. Optimization technology is ubiquitous in our society. It schedules planes and their crews, coordinates the production of steel, and organizes the transportation of iron ore from the mines to the ports. Optimization clears the day-ahead and real-time markets to deliver electricity to millions of people. It organizes kidney exchanges and cancer treatments and helps scientists understand the fundamental fabric of life, control complex chemical reactions, and design drugs that may benefit billions of individuals. This class is an introduction to discrete optimization and exposes students to some of the most fundamental concepts and algorithms in the field. It covers constraint programming, local search, and mixed-integer programming from their foundations to their applications for complex practical problems in areas such as scheduling, vehicle routing, supply-chain optimization, and resource allocation.
Discrete Optimization course image
Reviews 9/10 stars
34 Reviews for Discrete Optimization

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Nils Koesters profile image
Nils Koesters profile image
9/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 2 months ago
I have completed a couple of coursera MOOCs involving programming assignments. I had no knowledge of discrete optimization before. The course has several assignments, which are have from 6 to 8 sub-assignments. Once you cracked the first you WILL have to change tactic in between as the problems become increasingly difficult. Carleton and the Prof have tirelessly answered the forums and presented mail bags (weekly videos) about the progress of the class. You will also get data and feedback in graph form, how others perform, there are graphs and histograms about the results and the overall progress of the class. You feel part of a class because of this. This is not some Prof just throwing mindlessly stuff at you in a monotonous voice. The videos are technically great, Pascal is really enthusiastic and Carleton his TA is the most dedicated and helpful TA you will experience. The University of Melbourne seems to have put professional vid... I have completed a couple of coursera MOOCs involving programming assignments. I had no knowledge of discrete optimization before. The course has several assignments, which are have from 6 to 8 sub-assignments. Once you cracked the first you WILL have to change tactic in between as the problems become increasingly difficult. Carleton and the Prof have tirelessly answered the forums and presented mail bags (weekly videos) about the progress of the class. You will also get data and feedback in graph form, how others perform, there are graphs and histograms about the results and the overall progress of the class. You feel part of a class because of this. This is not some Prof just throwing mindlessly stuff at you in a monotonous voice. The videos are technically great, Pascal is really enthusiastic and Carleton his TA is the most dedicated and helpful TA you will experience. The University of Melbourne seems to have put professional video editing in place as one can see in a video that is posted elsewhere. The free approach leads to a practical approach. In the end I didn't watch all lectures as I figured out what seem to be the important techniques. I like practical, result orientated approaches, after all you also have to optimize your learning too. Off all the classes I have completed and all the classes I dropped as they were boring or uninspired this is by far the best MOOC I have experienced so far. But do not take this class, if you have few programming experience, have difficulties compiling open source software and are of a mindset that all has to be delivered on a plate. This class is for self starters that do not give up and have enough time to dedicate to it. Given the data presented by the DO team. ~4000 started and 795 received a certificate. That is a ~1/5 pass rate. Of the 795 passes ~0.48% received distinction. I deducted 1/2 a point as there is just too much material to learn it all. You don't need all to pass though, which might be a lecture in itsself,
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drew verlee profile image
drew verlee profile image
7/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 3 months ago
Unless i miss my mark this is a graduate level class in Computer Science. Successful students will most likely be near or above that level. Discrete Optimization is an introduction to solving some of the most difficult problems in computer science. So be ready for: 1) upwards of 12+ hours a week 2) increasingly demanding assignments 3) no hand holding. A wonderful overview that really teaches you the how to think about the problems in the right way. Your going to have to reach out to a lot of other resources to pass the later assignments. To give perspective to the one star review i offer this: Student:. I think that there is a gap between the lectures and the exercises. Put differently I feel that we are being left alone with only some general hints to implement complex algorithms. Professor: This is intentional of course. What we are trying to do is to make you experiment the techniques by yourself, and not just give an algorithm t... Unless i miss my mark this is a graduate level class in Computer Science. Successful students will most likely be near or above that level. Discrete Optimization is an introduction to solving some of the most difficult problems in computer science. So be ready for: 1) upwards of 12+ hours a week 2) increasingly demanding assignments 3) no hand holding. A wonderful overview that really teaches you the how to think about the problems in the right way. Your going to have to reach out to a lot of other resources to pass the later assignments. To give perspective to the one star review i offer this: Student:. I think that there is a gap between the lectures and the exercises. Put differently I feel that we are being left alone with only some general hints to implement complex algorithms. Professor: This is intentional of course. What we are trying to do is to make you experiment the techniques by yourself, and not just give an algorithm that everyone can implement. We would like you to understand the techniques or existing solvers in some depth and also discover which approaches work and which do not on each problem (several may work on the same problem but for different instances). The point being is that you need to come ready to put in a lot of time. If you can climb this mountain of a class your going to be able to see very far indeed. I, alais wasn't ready.
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Chad Colgur profile image
Chad Colgur profile image
8/10 starsCompleted
  • 7 reviews
  • 7 completed
6 years, 1 month ago
Time management is key to success in this course. Prior programming experience is mandatory, prior exposure to the field definitely useful. This course is relatively unstructured compared with others on Coursera. Some people found the lack of structure liberating. I'm probably a bit too used to being spoon fed material in the MOOC format. I don't think the format detracts from this course, it just didn't really fit my work flow very well. I really enjoyed the passion for the subject among staff. The focus on practical skills and "war stories" gave me a sense of glimpsing into the field. Some students rose to the programming challenge and that was also inspiring to read and work through. Pressed for a dislike I would have to say that the starter code was a little bit too trivial. Greedy was almost always the way to start each problem. The starter code could have been closer to that solution without giving away a lot.
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Student

2/10 starsDropped
6 years, 4 months ago
A good course only for students with strong knowledge on basic optimization algorithms. Logically it's impossible for students new to the topics, to learn all the (1) constraint programming, (2) local search and (3) integer programming in just 7 weeks. In a regular class, it needs one semester for each topic, and this short course trying to deliver 3 topics in one. Sorry, but this is more a mission impossible for students new to the topics. And we don't have a well structured course. It's more like a competition than a course. Below quoted from the course's forum, a feedback from student I agree very much: "We feel that we are being left alone with only some general hints to implement complex algorithms. We would like to see more in-depth explanations on how to apply the material to the given problems. Especially more advanced techniques would then be more accessible to the average student. Additionally I find the lectures a bit shor... A good course only for students with strong knowledge on basic optimization algorithms. Logically it's impossible for students new to the topics, to learn all the (1) constraint programming, (2) local search and (3) integer programming in just 7 weeks. In a regular class, it needs one semester for each topic, and this short course trying to deliver 3 topics in one. Sorry, but this is more a mission impossible for students new to the topics. And we don't have a well structured course. It's more like a competition than a course. Below quoted from the course's forum, a feedback from student I agree very much: "We feel that we are being left alone with only some general hints to implement complex algorithms. We would like to see more in-depth explanations on how to apply the material to the given problems. Especially more advanced techniques would then be more accessible to the average student. Additionally I find the lectures a bit short and superficial. Going into more detail would benefit the course. "
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