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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/algorithms-on-graphs
课程评论：没有评论
课程名称：图算法 课程概述： 在本课程中，您将了解图的基本概念及其在各种真实世界场景中的应用，例如道路网络、计算机网络以及社交网络。如果您使用过导航服务来寻找最佳路线，您实际上就是在使用图算法。本课程将带您深入学习图的性质、遍历图的方法以及在遍历过程中如何高效利用算法。您将学习最短路径算法，从基础算法到谷歌地图等导航服务所使用的先进算法。此课程还涵盖最小生成树的概念，这在道路、电话和计算机网络的规划中非常重要，并且在数据聚类和近似算法中也有应用。 课程大纲： 1. **图的分解 1**：了解图的表示方法以及基础的分解算法，进行迷宫探索、计算机科学课程分析和道路网络分析的编程作业。 2. **图的分解 2**：继续研究有向图的分解算法。 3. **图中的路径 1**：学习图中最短路径的算法，包括广度优先搜索、Dijkstra算法和Bellman-Ford算法，并了解其在导航和航班排序等实际应用。 4. **图中的路径 2**：继续学习最短路径算法，重点了解Dijkstra算法和Bellman-Ford算法的应用。 5. **最小生成树**：研究最小生成树问题，学习Kruskal和Prim两种贪心算法，并进行城市之间道路构建和对象聚类的编程作业。 6. **高级最短路径项目（可选）**：学习在实际工程中性能极其优越的高级最短路径算法，并在项目中应用这些算法找到真实地图上的最短路径。 通过本课程，您将掌握图及其算法的核心知识，为处理复杂网络问题打下坚实基础。
Name:Decomposition of Graphs 1
Description:Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders hot in Facebook, you're going to work with graphs and algorithms on graphs. In this module, you will learn ways to represent a graph as well as basic algorithms for decomposing graphs into parts. In the programming assignment of this module, you will apply the algorithms that you’ve learned to implement efficient programs for exploring mazes, analyzing Computer Science curriculum, and analyzing road networks. In the first week of the module, we focus on undirected graphs.
Name:Decomposition of Graphs 2
Description:This week we continue to study graph decomposition algorithms, but now for directed graphs.
Name:Paths in Graphs 1
Description:In this module you will study algorithms for finding Shortest Paths in Graphs. These algorithms have lots of applications. When you launch a navigation app on your smartphone like Google Maps or Yandex.Navi, it uses these algorithms to find you the fastest route from work to home, from home to school, etc. When you search for airplane tickets, these algorithms are used to find a route with the minimum number of plane changes. Unexpectedly, these algorithms can also be used to determine the optimal way to do currency exchange, sometimes allowing to earh huge profit! We will cover all these applications, and you will learn Breadth-First Search, Dijkstra's Algorithm and Bellman-Ford Algorithm. These algorithms are efficient and lay the foundation for even more efficient algorithms which you will learn and implement in the Shortest Paths Capstone Project to find best routes on real maps of cities and countries, find distances between people in Social Networks. In the end you will be able to find Shortest Paths efficiently in any Graph. This week we will study Breadth-First Search algorithm.
Name:Paths in Graphs 2
Description:This week we continue to study Shortest Paths in Graphs. You will learn Dijkstra's Algorithm which can be applied to find the shortest route home from work. You will also learn Bellman-Ford's algorithm which can unexpectedly be applied to choose the optimal way of exchanging currencies. By the end you will be able to find shortest paths efficiently in any Graph.
Name:Minimum Spanning Trees
Description:In this module, we study the minimum spanning tree problem. We will cover two elegant greedy algorithms for this problem: the first one is due to Kruskal and uses the disjoint sets data structure, the second one is due to Prim and uses the priority queue data structure. In the programming assignment for this module you will be computing an optimal way of building roads between cities and an optimal way of partitioning a given set of objects into clusters (a fundamental problem in data mining).
Name:Advanced Shortest Paths Project (Optional)
Description:In this module, you will learn Advanced Shortest Paths algorithms that work in practice 1000s (up to 25000) of times faster than the classical Dijkstra's algorithm on real-world road networks and social networks graphs. You will work on a Programming Project based on these algorithms. You will find the shortest paths on the real maps of parts of US and the shortest paths connecting people in the social networks. We encourage you not only to use the ideas from this module's lectures in your implementations, but also to come up with your own ideas for speeding up the algorithm! We encourage you to compete on the forums to see whose implementation is the fastest one :)
If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect a set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs. In this online course, you will first learn what a graph is and what are some of the most important properties. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. We will then talk about shortest paths algorithms — from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. We will finish with minimum spanning trees which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms.
