算法

Algorithms

Learn To Think Like A Computer Scientist
Master the fundamentals of the design and analysis of algorithms.

斯坦福大学

专项课程

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  • 分类: 计算机
  • 平台: Coursera
  • 语言: 英语

本专项课程介绍

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.

第 1 门课程

Divide and Conquer, Sorting and Searching, and Randomized Algorithms

即将开课的班次:Jun 5 — Jul 10。
课程学习时间
4 weeks of study, 4-8 hours/week

课程概述
The primary topics in this part of the specialization are: asymptotic (“Big-oh”) notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

第 2 门课程

Graph Search, Shortest Paths, and Data Structures

即将开课的班次:Jun 5 — Jul 10。
课程学习时间
4 weeks of study, 4-8 hours/week

课程概述
The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

第 3 门课程

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

即将开课的班次:Jun 5 — Jul 10。
课程学习时间
4 weeks of study, 4-8 hours/week

课程概述
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).

第 4 门课程

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

当前班次:May 29 — Jul 3。
课程学习时间
4 weeks of study, 4-8 hours/week

课程概述
The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

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