This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.
Este curso faz parte do Programa de cursos integrados Data Science Foundations: Data Structures and Algorithms
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Programa de cursos integrados Data Science Foundations: Data Structures and AlgorithmsUniversidade do Colorado em BoulderInformações sobre o curso
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Prazos flexÃveis
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NÃvel avançado
Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.
Aprox. 38 horas para completar
Inglês
Legendas: Inglês
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Experimente o Coursera for BusinessO que você vai aprender
Describe basic algorithm design techniques
Create divide and conquer, dynamic programming, and greedy algorithms
Understand intractable problems, P vs NP and the use of integer programming solvers to tackle some of these problems
Habilidades que você terá
- Analysis of Algorithms
- Algorithm Design
- Python Programming
- Data Structure Design
- Intractability
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel avançado
Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.
Aprox. 38 horas para completar
Inglês
Legendas: Inglês
Sua empresa se beneficiaria do treinamento dos funcionários em habilidades sob demanda?
Experimente o Coursera for Business
oferecido por
Universidade do Colorado em Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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Este curso é parte de um programa de graduação on-line oferecido por Universidade do Colorado em Boulder. Quando você se inscreve através de uma universidade em um curso sem diploma, mas que conta com créditos e o completa on-line, ele conta como horas complementares na graduação da CU-Boulder. Você só precisa se inscrever pela universidade.
Programa - O que você aprenderá com este curso
11 horas para concluir
Divide and Conquer Algorithms
We will formally cover divide and conquer algorithms as a design scheme and look at some divide and conquer algorithms we have encountered in the past. We will learn some divide and conquer algorithms for Integer Multiplication (Karatsuba’s Algorithm), Matrix Multiplication (Strassen’s Algorithm), Fast Fourier Transforms (FFTs), and Finding Closest Pair of Points.
11 horas para concluir
9 vÃdeos (Total 231 mÃn.), 11 leituras, 6 testes
9 horas para concluir
Dynamic Programming Algorithms
In this module, you will learn about dynamic programming as a design principle for algorithms. We will provide a step-by-step approach to formulating a problem as a dynamic program and solving these problems using memoization. We will cover dynamic programming for finding longest common subsequences, Knapsack problem and some interesting dynamic programming applications.
9 horas para concluir
6 vÃdeos (Total 131 mÃn.), 6 leituras, 6 testes
7 horas para concluir
Greedy Algorithms
In this module, we will learn about greedy algorithms. We will understand the basic design principles for greedy algorithms and learn about a few algorithms for greedy scheduling and Huffman codes. We will also learn some interesting cases when being greedy provides a guaranteed approximation to the actual solution.
7 horas para concluir
5 vÃdeos (Total 102 mÃn.), 4 leituras, 4 testes
11 horas para concluir
Intractability and Supplement on Quantum Computing
P vs NP, Examples such as Travelling Salesperson Problem, Vertex Cover, 3-Coloring and others; Integer Linear Programming and Translating Problems into Integer Programming.
11 horas para concluir
9 vÃdeos (Total 288 mÃn.), 5 leituras, 5 testes
Avaliações
- 5 stars70,58%
- 4 stars17,64%
- 3 stars2,94%
- 2 stars2,94%
- 1 star5,88%
Principais avaliações do DYNAMIC PROGRAMMING, GREEDY ALGORITHMS
por AM18 de set de 2022
Great work from professor Sriram Sankaranarayanan explaining such complex material. I wish we could review more examples during the class (specially Dynamic Programming ones).
por DM20 de set de 2021
Excellent. This course covers some difficult topics, but the lectures and homework assignments were superb and made them quite approachable.
por YS22 de jul de 2022
Excellent course! I really learned alot and enjoyed all the challenges and topics in your course. Thank you so much!
Sobre Programa de cursos integrados Data Science Foundations: Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.
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