Об этом курсе
Оценки: 298
Рецензии: 64

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Прибл. 23 часа на выполнение

Предполагаемая нагрузка: 4 weeks of study, 4-8 hours/week...


Субтитры: Английский

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Python ProgrammingLinear Programming (LP)Np-CompletenessDynamic Programming

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Прибл. 23 часа на выполнение

Предполагаемая нагрузка: 4 weeks of study, 4-8 hours/week...


Субтитры: Английский

Программа курса: что вы изучите

5 ч. на завершение

Flows in Networks

Network flows show up in many real world situations in which a good needs to be transported across a network with limited capacity. You can see it when shipping goods across highways and routing packets across the internet. In this unit, we will discuss the mathematical underpinnings of network flows and some important flow algorithms. We will also give some surprising examples on seemingly unrelated problems that can be solved with our knowledge of network flows....
9 видео ((всего 72 мин.)), 3 материалов для самостоятельного изучения, 2 тестов
9 видео
Network Flows9мин
Residual Networks10мин
The Ford–Fulkerson Algorithm7мин
Slow Example3мин
The Edmonds–Karp Algorithm11мин
Bipartite Matching11мин
Image Segmentation7мин
3 материала для самостоятельного изучения
Slides and Resources on Flows in Networks10мин
Available Programming Languages10мин
FAQ on Programming Assignments10мин
1 практическое упражнение
Flow Algorithms10мин
5 ч. на завершение

Linear Programming

Linear programming is a very powerful algorithmic tool. Essentially, a linear programming problem asks you to optimize a linear function of real variables constrained by some system of linear inequalities. This is an extremely versatile framework that immediately generalizes flow problems, but can also be used to discuss a wide variety of other problems from optimizing production procedures to finding the cheapest way to attain a healthy diet. Surprisingly, this very general framework admits efficient algorithms. In this unit, we will discuss some of the importance of linear programming problems along with some of the tools used to solve them....
10 видео ((всего 84 мин.)), 1 материал для самостоятельного изучения, 2 тестов
10 видео
Linear Programming8мин
Linear Algebra: Method of Substitution5мин
Linear Algebra: Gaussian Elimination10мин
(Optional) Duality Proofs7мин
Linear Programming Formulations8мин
The Simplex Algorithm10мин
(Optional) The Ellipsoid Algorithm6мин
1 материал для самостоятельного изучения
Slides and Resources on Linear Programming10мин
1 практическое упражнение
Linear Programming Quiz10мин
5 ч. на завершение

NP-complete Problems

Although many of the algorithms you've learned so far are applied in practice a lot, it turns out that the world is dominated by real-world problems without a known provably efficient algorithm. Many of these problems can be reduced to one of the classical problems called NP-complete problems which either cannot be solved by a polynomial algorithm or solving any one of them would win you a million dollars (see Millenium Prize Problems) and eternal worldwide fame for solving the main problem of computer science called P vs NP. It's good to know this before trying to solve a problem before the tomorrow's deadline :) Although these problems are very unlikely to be solvable efficiently in the nearest future, people always come up with various workarounds. In this module you will study the classical NP-complete problems and the reductions between them. You will also practice solving large instances of some of these problems despite their hardness using very efficient specialized software based on tons of research in the area of NP-complete problems....
16 видео ((всего 115 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
16 видео
Search Problems9мин
Traveling Salesman Problem7мин
Hamiltonian Cycle Problem8мин
Longest Path Problem1мин
Integer Linear Programming Problem3мин
Independent Set Problem3мин
P and NP4мин
Showing NP-completeness6мин
Independent Set to Vertex Cover5мин
3-SAT to Independent Set14мин
SAT to 3-SAT7мин
Circuit SAT to SAT12мин
All of NP to Circuit SAT5мин
Using SAT-solvers14мин
2 материала для самостоятельного изучения
Slides and Resources on NP-complete Problems10мин
Minisat Installation Guide10мин
1 практическое упражнение
NP-complete Problems12мин
5 ч. на завершение

Coping with NP-completeness

After the previous module you might be sad: you've just went through 5 courses in Algorithms only to learn that they are not suitable for most real-world problems. However, don't give up yet! People are creative, and they need to solve these problems anyway, so in practice there are often ways to cope with an NP-complete problem at hand. We first show that some special cases on NP-complete problems can, in fact, be solved in polynomial time. We then consider exact algorithms that find a solution much faster than the brute force algorithm. We conclude with approximation algorithms that work in polynomial time and find a solution that is close to being optimal. ...
11 видео ((всего 119 мин.)), 1 материал для самостоятельного изучения, 2 тестов
11 видео
2-SAT: Algorithm12мин
Independent Sets in Trees14мин
3-SAT: Backtracking11мин
3-SAT: Local Search12мин
TSP: Dynamic Programming15мин
TSP: Branch and Bound9мин
Vertex Cover9мин
Metric TSP12мин
TSP: Local Search6мин
1 материал для самостоятельного изучения
Slides and Resources on Coping with NP-completeness10мин
1 практическое упражнение
Coping with NP-completeness6мин
Рецензии: 64Chevron Right


начал новую карьеру, пройдя эти курсы


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стал больше зарабатывать или получил повышение

Лучшие рецензии

автор: EMJan 4th 2018

As usual, complex arguments explained in simple terms!\n\nSome problems are really tough! (e.g. there's a problem from Google Code Jam).\n\nThank you for this course!

автор: YCMay 15th 2019

This is a very challenging course in the specialization. I learned a lot form going through the programming assignments!



Alexander S. Kulikov

Visiting Professor
Department of Computer Science and Engineering

Michael Levin

Computer Science

Daniel M Kane

Assistant Professor
Department of Computer Science and Engineering / Department of Mathematics

Neil Rhodes

Adjunct Faculty
Computer Science and Engineering

О Калифорнийский университет в Сан-Диего

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory....

О Национальный исследовательский университет "Высшая школа экономики"

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more. Learn more on www.hse.ru...

О специализации ''Структуры и алгоритмы данных'

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine....
Структуры и алгоритмы данных

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