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).
Este curso forma parte de Programa Especializado - Algoritmos
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
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Acerca de este Curso
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Habilidades que obtendrás
Spanning TreeAlgorithmsDynamic ProgrammingGreedy Algorithm
Los estudiantes que toman este Course son
- Machine Learning Engineers
- Software Engineers
- Data Scientists
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Programa - Qué aprenderás en este curso
Semana
14 horas para completar
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.
16 videos (Total 160 minutos), 4 lecturas, 2 cuestionarios
16 videos
Application: Sequence Alignment8m
Introduction to Greedy Algorithms12m
Application: Optimal Caching10m
Problem Definition5m
A Greedy Algorithm12m
Correctness Proof - Part I6m
Correctness Proof - Part II4m
Handling Ties [Advanced - Optional]7m
MST Problem Definition11m
Prim's MST Algorithm7m
Correctness Proof I15m
Correctness Proof II8m
Proof of Cut Property [Advanced - Optional]11m
Fast Implementation I14m
Fast Implementation II9m
4 lecturas
Week 1 Overview10m
Overview, Resources, and Policies10m
Lecture slides10m
Optional Theory Problems (Week 1)10m
2 ejercicios de práctica
Problem Set #110m
Programming Assignment #16m
Semana
24 horas para completar
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).
16 videos (Total 188 minutos), 2 lecturas, 2 cuestionarios
16 videos
Correctness of Kruskal's Algorithm9m
Implementing Kruskal's Algorithm via Union-Find I9m
Implementing Kruskal's Algorithm via Union-Find II13m
MSTs: State-of-the-Art and Open Questions [Advanced - Optional]9m
Application to Clustering11m
Correctness of Clustering Algorithm9m
Lazy Unions [Advanced - Optional]10m
Union-by-Rank [Advanced - Optional]12m
Analysis of Union-by-Rank [Advanced - Optional]14m
Path Compression [Advanced - Optional]14m
Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional]9m
Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional]11m
The Ackermann Function [Advanced - Optional]16m
Path Compression: Tarjan's Analysis I [Advanced - Optional]14m
Path Compression: Tarjan's Analysis II [Advanced - Optional]13m
2 lecturas
Week 2 Overview10m
Optional Theory Problems (Week 2)10m
2 ejercicios de práctica
Problem Set #210m
Programming Assignment #24m
Semana
32 horas para completar
Week 3
Huffman codes; introduction to dynamic programming.
11 videos (Total 105 minutos), 1 lectura, 2 cuestionarios
11 videos
Problem Definition10m
A Greedy Algorithm16m
A More Complex Example4m
Correctness Proof I10m
Correctness Proof II12m
Introduction: Weighted Independent Sets in Path Graphs7m
WIS in Path Graphs: Optimal Substructure9m
WIS in Path Graphs: A Linear-Time Algorithm9m
WIS in Path Graphs: A Reconstruction Algorithm6m
Principles of Dynamic Programming7m
1 lectura
Week 3 Overview10m
2 ejercicios de práctica
Problem Set #310m
Programming Assignment #36m
Semana
43 horas para completar
Week 4
Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees.
10 videos (Total 107 minutos), 3 lecturas, 3 cuestionarios
10 videos
A Dynamic Programming Algorithm9m
Example [Review - Optional]12m
Optimal Substructure13m
A Dynamic Programming Algorithm12m
Problem Definition12m
Optimal Substructure9m
Proof of Optimal Substructure6m
A Dynamic Programming Algorithm I9m
A Dynamic Programming Algorithm II9m
3 lecturas
Week 4 Overview10m
Optional Theory Problems (Week 4)10m
Info and FAQ for final exam10m
3 ejercicios de práctica
Problem Set #410m
Programming Assignment #44m
Final Exam20m
38%
comenzó una nueva carrera después de completar estos cursos
45%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
14%
consiguió un aumento de sueldo o ascenso
Principales revisiones sobre Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
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.
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.
Instructor
Tim Roughgarden
ProfessorComputer Science
Acerca de Universidad 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.
Acerca de Programa especializado 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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
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