The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
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Programa de cursos integrados AlgoritmosUniversidade de StanfordInformações sobre o curso
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Resultados de carreira do aprendiz
38%
comecei uma nova carreira após concluir estes cursos
45%
consegui um benefício significativo de carreira com este curso
14%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
Spanning TreeAlgorithmsDynamic ProgrammingGreedy Algorithm
Resultados de carreira do aprendiz
38%
comecei uma nova carreira após concluir estes cursos
45%
consegui um benefício significativo de carreira com este curso
14%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade de Stanford
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Programa - O que você aprenderá com este curso
4 horas para concluir
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.
4 horas para concluir
16 vídeos (Total 160 mín.), 4 leituras, 2 testes
16 videos
Application: Sequence Alignment8min
Introduction to Greedy Algorithms12min
Application: Optimal Caching10min
Problem Definition5min
A Greedy Algorithm12min
Correctness Proof - Part I6min
Correctness Proof - Part II4min
Handling Ties [Advanced - Optional]7min
MST Problem Definition11min
Prim's MST Algorithm7min
Correctness Proof I15min
Correctness Proof II8min
Proof of Cut Property [Advanced - Optional]11min
Fast Implementation I14min
Fast Implementation II9min
4 leituras
Week 1 Overview10min
Overview, Resources, and Policies10min
Lecture slides10min
Optional Theory Problems (Week 1)10min
2 exercícios práticos
Problem Set #110min
Programming Assignment #16min
4 horas para concluir
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).
4 horas para concluir
16 vídeos (Total 188 mín.), 2 leituras, 2 testes
16 videos
Correctness of Kruskal's Algorithm9min
Implementing Kruskal's Algorithm via Union-Find I9min
Implementing Kruskal's Algorithm via Union-Find II13min
MSTs: State-of-the-Art and Open Questions [Advanced - Optional]9min
Application to Clustering11min
Correctness of Clustering Algorithm9min
Lazy Unions [Advanced - Optional]10min
Union-by-Rank [Advanced - Optional]12min
Analysis of Union-by-Rank [Advanced - Optional]14min
Path Compression [Advanced - Optional]14min
Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional]9min
Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional]11min
The Ackermann Function [Advanced - Optional]16min
Path Compression: Tarjan's Analysis I [Advanced - Optional]14min
Path Compression: Tarjan's Analysis II [Advanced - Optional]13min
2 leituras
Week 2 Overview10min
Optional Theory Problems (Week 2)10min
2 exercícios práticos
Problem Set #210min
Programming Assignment #24min
2 horas para concluir
Week 3
Huffman codes; introduction to dynamic programming.
2 horas para concluir
11 vídeos (Total 105 mín.), 1 leitura, 2 testes
11 videos
Problem Definition10min
A Greedy Algorithm16min
A More Complex Example4min
Correctness Proof I10min
Correctness Proof II12min
Introduction: Weighted Independent Sets in Path Graphs7min
WIS in Path Graphs: Optimal Substructure9min
WIS in Path Graphs: A Linear-Time Algorithm9min
WIS in Path Graphs: A Reconstruction Algorithm6min
Principles of Dynamic Programming7min
1 leituras
Week 3 Overview10min
2 exercícios práticos
Problem Set #310min
Programming Assignment #36min
3 horas para concluir
Week 4
Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees.
3 horas para concluir
10 vídeos (Total 107 mín.), 3 leituras, 3 testes
10 videos
A Dynamic Programming Algorithm9min
Example [Review - Optional]12min
Optimal Substructure13min
A Dynamic Programming Algorithm12min
Problem Definition12min
Optimal Substructure9min
Proof of Optimal Substructure6min
A Dynamic Programming Algorithm I9min
A Dynamic Programming Algorithm II9min
3 leituras
Week 4 Overview10min
Optional Theory Problems (Week 4)10min
Info and FAQ for final exam10min
3 exercícios práticos
Problem Set #410min
Programming Assignment #44min
Final Exam20min
Avaliações
Principais avaliações do GREEDY ALGORITHMS, MINIMUM SPANNING TREES, AND DYNAMIC PROGRAMMING
por RG26 de Abr de 2020
I learned more about Greedy Algorithms and Algorithms to compute MST's and their importance on real world problems. Also I got to start to understand Dynamic Programming in a way I didn't before.
por FC29 de Jun de 2018
Tim Roughgarden is a fantastic teacher! He really cares about explaining in a simple manner and also give you useful hints and advices to dive deeper in all the topics of the course. Thanks, Tim!
por SW24 de Fev de 2019
One of the best courses to make a student learn DP in a way that enables him/her to think of the subproblems and way to proceed to solving these subproblems. Definitely helpful for me. Thanks.
por NT13 de Jun de 2019
As usual with Stanford and Tim Roughgarden, a high-quality course with an informal style but a lot of rigor. The assignments are challenging but doable. Highly recommended.
Sobre Programa de cursos integrados Algoritmos
Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.
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