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.
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数据结构与算法 專項課程加州大学圣地亚哥分校課程信息
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第 3 門課程（共 6 門）
中級
完成時間大約為55 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
- Graph Theory
- Graphs
- Graph Algorithms
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 6 門）
中級
完成時間大約為55 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
加州大学圣地亚哥分校
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 5 小時
Decomposition of Graphs 1
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.
完成時間為 5 小時
5 個視頻 （總計 43 分鐘）, 5 個閱讀材料, 1 個測驗
完成時間為 4 小時
Decomposition of Graphs 2
This week we continue to study graph decomposition algorithms, but now for directed graphs.
完成時間為 4 小時
4 個視頻 （總計 36 分鐘）, 1 個閱讀材料, 1 個測驗
完成時間為 4 小時
Paths in Graphs 1
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.
完成時間為 4 小時
8 個視頻 （總計 56 分鐘）, 1 個閱讀材料, 1 個測驗
完成時間為 5 小時
Paths in Graphs 2
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.
完成時間為 5 小時
13 個視頻 （總計 90 分鐘）, 2 個閱讀材料, 1 個測驗
審閱
- 5 stars79.15%
- 4 stars16.81%
- 3 stars2.66%
- 2 stars0.84%
- 1 star0.51%
來自ALGORITHMS ON GRAPHS的熱門評論
由 AD 提供2020年6月23日
This course is very well designed, starting from basic concepts and thoroughly covering all Graph relevant Algorithms. A big thank you to all Professors.
由 ED 提供2021年4月15日
This is my favorite course in the specialization, the lectures are really clear and the programming assignments are fun and really help to deeply understand everything
由 AS 提供2020年8月9日
Explanations were as clear as possible. The problem sets were nice enough. It would be excellent if some advanced topics (like Flow) could be added to this particular course.
由 FS 提供2020年9月17日
This course helps me to a better understanding of Graph Theory. The exercise was a little bit difficult but it can help me to gain more knowledge to solve these problem.
關於 数据结构与算法 專項課程
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
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