你将学到什么
Algorithms
Dynamic Programming
Greedy Algorithm
Divide And Conquer Algorithms
课程概况
这门课中，你将学到算法设计的多种基本原理。你将学到分治法设计范式，了解它在快速排序、搜索及乘法中的应用。你将学到多种在图上极快速进行计算的基本方法，例如计算连通性信息和最短路径。最后，我们将学习，让电脑“抛硬币”如何引出优雅而实用的算法和数据结构。我们要探讨的问题包括：堆、哈希表、布隆过滤器和平衡搜索树这样的数据结构是如何工作的？为什么快速排序能运行得这么快？关于网络结构和社交网络，图算法能告诉我们一些什么知识？三年级老师所教的乘法算法只是次优算法吗？
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.
包含课程
课程1
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
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).
课程2
Graph Search, Shortest Paths, and Data Structures
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 de...
课程3
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
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).
课程4
Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
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).
预备知识
至少知道如何使用一种语言进行编程（如C语言、Java或Python）；熟悉证明方法，包括归纳法和反证法证明。 在斯坦福大学，这门课的另一版本被提供给计算机科学专业大二、大三、大四水平的同学学习。
