In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
About this Course
Skills you will gain
- 5 stars72.30%
- 4 stars18.30%
- 3 stars5.53%
- 2 stars1.69%
- 1 star2.15%
TOP REVIEWS FROM ADVANCED ALGORITHMS AND COMPLEXITY
The problems are really challenging, thank you! However, the instructor is not very active in the discussion forum, which is a pity when you really need help and get stucked in the problem set.
Thank you so much. You are doing such a great work but i appreciate if you explain week-2 (linear programming) in detail. Thank you.
Really rigorous and fundamental with what scientist and other professionals need to know about programming.
Very Very Challenging Course , it test your patience and rewards is extremely satisfying. Lot of learning on a complicated subject of NP-Hard problems.
About the Data Structures and Algorithms Specialization
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.