About this Course
최근 조회 23,164

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 23시간 필요

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

영어

자막: 영어

귀하가 습득할 기술

Python ProgrammingLinear Programming (LP)Np-CompletenessDynamic Programming

다음 전문 분야의 1개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 23시간 필요

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

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 5시간 필요

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 videos (Total 72 min), 3 readings, 2 quizzes
9개의 동영상
Maxflow-Mincut7m
The Ford–Fulkerson Algorithm7m
Slow Example3m
The Edmonds–Karp Algorithm11m
Bipartite Matching11m
Image Segmentation7m
3개의 읽기 자료
Slides and Resources on Flows in Networks10m
Available Programming Languages10m
FAQ on Programming Assignments10m
1개 연습문제
Flow Algorithms10m
2
완료하는 데 5시간 필요

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 videos (Total 84 min), 1 reading, 2 quizzes
10개의 동영상
Linear Algebra: Gaussian Elimination10m
Convexity9m
Duality12m
(Optional) Duality Proofs7m
Linear Programming Formulations8m
The Simplex Algorithm10m
(Optional) The Ellipsoid Algorithm6m
1개의 읽기 자료
Slides and Resources on Linear Programming10m
1개 연습문제
Linear Programming Quiz10m
3
완료하는 데 5시간 필요

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 videos (Total 115 min), 2 readings, 2 quizzes
16개의 동영상
Hamiltonian Cycle Problem8m
Longest Path Problem1m
Integer Linear Programming Problem3m
Independent Set Problem3m
P and NP4m
Reductions5m
Showing NP-completeness6m
Independent Set to Vertex Cover5m
3-SAT to Independent Set14m
SAT to 3-SAT7m
Circuit SAT to SAT12m
All of NP to Circuit SAT5m
Using SAT-solvers14m
2개의 읽기 자료
Slides and Resources on NP-complete Problems10m
Minisat Installation Guide10m
1개 연습문제
NP-complete Problems12m
4
완료하는 데 5시간 필요

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 videos (Total 119 min), 1 reading, 2 quizzes
11개의 동영상
Independent Sets in Trees14m
3-SAT: Backtracking11m
3-SAT: Local Search12m
TSP: Dynamic Programming15m
TSP: Branch and Bound9m
Vertex Cover9m
Metric TSP12m
TSP: Local Search6m
1개의 읽기 자료
Slides and Resources on Coping with NP-completeness10m
1개 연습문제
Coping with NP-completeness6m
4.6
69개의 리뷰Chevron Right

40%

이 강좌를 수료한 후 새로운 경력 시작하기

57%

이 강좌를 통해 확실한 경력상 이점 얻기

25%

급여 인상 또는 승진하기

Advanced Algorithms and Complexity의 최상위 리뷰

대학: 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!

대학: ASJun 15th 2018

Another great course in this specialization with challenging and interesting assignments. However, this one is somewhat harder but rewarding.

강사

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Alexander S. Kulikov

Visiting Professor
Department of Computer Science and Engineering
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Michael Levin

Lecturer
Computer Science
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Daniel M Kane

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

Adjunct Faculty
Computer Science and Engineering

캘리포니아 샌디에고 대학교 정보

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....

국립 연구 고등 경제 대학 정보

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...

데이터 구조 및 알고리즘 전문 분야 정보

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....
데이터 구조 및 알고리즘

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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