How does Google Maps plan the best route for getting around town given current traffic conditions? How does an internet router forward packets of network traffic to minimize delay? How does an aid group allocate resources to its affiliated local partners? To solve such problems, we first represent the key pieces of data in a complex data structure. In this course, you’ll learn about data structures, like graphs, that are fundamental for working with structured real world data. You will develop, implement, and analyze algorithms for working with this data to solve real world problems. In addition, as the programs you develop in this course become more complex, we’ll examine what makes for good code and class hierarchy design so that you can not only write correct code, but also share it with other people and maintain it in the future. The backbone project in this course will be a route planning application. You will apply the concepts from each Module directly to building an application that allows an autonomous agent (or a human driver!) to navigate its environment. And as usual we have our different video series to help tie the content back to its importance in the real world and to provide tiered levels of support to meet your personal needs.
Core: Geographic Maps
您將學習的技能
Graphs, Search Algorithm, Graph Algorithms, Graph Data Structures
審閱
MK
Jul 18, 2017
Excellent course. I have a 32 year old CS degree and needed a Java Course which did not cover the basics of programming but rather covered advanced CS topics in a Java Context. This is the course.
BS
Jan 12, 2016
Splendidly covers path finding basics. Enjoyed the course.\n\nIt would be great to go into advanced algorithms useful for programming challenges (like SPOJ) with the same instructors.
In the past two weeks, you've developed a strong understanding of how to design classes to represent a graph and how to use a graph to represent a map. In this week, you'll add a key feature of map data to our graph representation -- distances -- by adding weights to your edges to produce a "weighted graph". Although this might seem like a small change, the algorithms that work for unweighted graphs may prove ineffective for weighted graphs. To address this problem, you'll explore more advanced shortest path algorithms. First, you'll see how to find the shortest path on a weighted graph, then you'll see how to find it more quickly. In the project, you'll apply these ideas to create the core of any good mapping application: finding the shortest route from one location to another.
教學方
Leo Porter
Associate Teaching Professor
Mia Minnes
Assistant Teaching Professor
Christine Alvarado
Associate Teaching Professor
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