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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/algorithmic-toolbox
课程评论：没有评论
课程名称：算法工具箱 课程概述：该在线课程涵盖了在实际应用中频繁出现的计算问题的基本算法技术和思想，包括排序与查找、分治法、贪心算法和动态规划。我们将学习许多理论知识：如何排序数据以及如何借助排序进行查找；如何将一个大问题拆分成小部分并递归地解决；何时使用贪心策略；动态规划在基因组研究中的应用。您将练习解决计算问题、设计新的算法，并高效地实现解决方案（确保运行时间不超过一秒）。 课程大纲： 1. **编程挑战** 描述：欢迎来到数据结构与算法的第一模块！我们将概述算法和数据结构的应用场景（提示：无处不在），并带您体验一些样例编程挑战。编程挑战是本专项的重要组成部分（而且通常是最困难的部分！），因为要完全理解一个算法，您需要实现它。编写正确和高效的程序是困难的；请不要惊讶于程序没有按计划工作——我们的第一个程序也没有成功！我们将辅助您完成此专项，通过展示如何实现您的第一个编程挑战。同时，我们将介绍测试技巧，以提高您第一次通过作业的几率。如果程序未按预期工作，我们将展示如何修复问题，即使您还不知道是哪个测试导致实现失败。 2. **算法热身** 描述：在这一模块中，您将学习到基于高效算法的程序可以比基于天真的算法快数十亿倍地解决相同问题。您将学习如何在不实现算法的情况下估算算法的运行时间和内存。掌握这一知识后，您将能够比较各种算法，选择最有效的算法，并最终在编程挑战中实现它们！ 3. **贪心算法** 描述：在本模块中，您将学习一种看似天真但强大的算法类——贪心算法。在了解贪心算法的核心思想后，您可能会觉得它是解决本课程几乎所有编程挑战的“瑞士军刀”。但要警惕：除了一些例外情况，这种直观的想法在实践中很少奏效！因此，重要的是在使用贪心算法之前证明其总能产生最优解。最后，我们将通过几个编程挑战来测试您的直觉和对贪心算法的理解。 4. **分治法** 描述：在这一模块中，您将学习一种强大的算法技术——分治法。基于这一技术，您将看到如何以比天真的线性查找快数百万倍的速度搜索庞大的数据库。您甚至会认识到，您在小学学到的标准乘法方法远非最快！随后，我们将应用分治法设计两个高效算法（归并排序和快速排序）来对浩大的列表进行排序，这个问题在实际中有许多应用。最后，我们将展示这两个算法的最优性，即没有算法能更快地排序！ 5. **动态规划 1** 描述：在课程的最后一个模块中，您将学习一种强大的算法技术——动态规划，用于解决许多优化问题。实际上，动态规划可以解决许多使用贪心或分治策略无法解决的问题。动态规划在实践中有无数应用：从最大化电视台的广告收入，到互联网页面的相似性搜索，再到基因发现（生物学家需要找出将一种基因转变为另一种基因所需的最小突变数）。您将学习如何利用这一思想自动进行拼写校正，并显示两种文本版本之间的差异。 6. **动态规划 2** 描述：在这个模块中，我们将继续练习实现动态规划的解决方案。
Name:Programming Challenges
Description:Welcome to the first module of Data Structures and Algorithms! Here we will provide an overview of where algorithms and data structures are used (hint: everywhere) and walk you through a few sample programming challenges. The programming challenges represent an important (and often the most difficult!) part of this specialization because the only way to fully understand an algorithm is to implement it. Writing correct and efficient programs is hard; please don’t be surprised if they don’t work as you planned—our first programs did not work either! We will help you on your journey through the specialization by showing how to implement your first programming challenges. We will also introduce testing techniques that will help increase your chances of passing assignments on your first attempt. In case your program does not work as intended, we will show how to fix it, even if you don’t yet know which test your implementation is failing on.
Name:Algorithmic Warm-up
Description:In this module you will learn that programs based on efficient algorithms can solve the same problem billions of times faster than programs based on naïve algorithms. You will learn how to estimate the running time and memory of an algorithm without even implementing it. Armed with this knowledge, you will be able to compare various algorithms, select the most efficient ones, and finally implement them as our programming challenges!
Name:Greedy Algorithms
Description:In this module you will learn about seemingly naïve yet powerful class of algorithms called greedy algorithms. After you will learn the key idea behind the greedy algorithms, you may feel that they represent the algorithmic Swiss army knife that can be applied to solve nearly all programming challenges in this course. But be warned: with a few exceptions that we will cover, this intuitive idea rarely works in practice! For this reason, it is important to prove that a greedy algorithm always produces an optimal solution before using this algorithm. In the end of this module, we will test your intuition and taste for greedy algorithms by offering several programming challenges.
Name:Divide-and-Conquer
Description:In this module you will learn about a powerful algorithmic technique called Divide and Conquer. Based on this technique, you will see how to search huge databases millions of times faster than using naïve linear search. You will even learn that the standard way to multiply numbers (that you learned in the grade school) is far from the being the fastest! We will then apply the divide-and-conquer technique to design two efficient algorithms (merge sort and quick sort) for sorting huge lists, a problem that finds many applications in practice. Finally, we will show that these two algorithms are optimal, that is, no algorithm can sort faster!
Name:Dynamic Programming 1
Description:In this final module of the course you will learn about the powerful algorithmic technique for solving many optimization problems called Dynamic Programming. It turned out that dynamic programming can solve many problems that evade all attempts to solve them using greedy or divide-and-conquer strategy. There are countless applications of dynamic programming in practice: from maximizing the advertisement revenue of a TV station, to search for similar Internet pages, to gene finding (the problem where biologists need to find the minimum number of mutations to transform one gene into another). You will learn how the same idea helps to automatically make spelling corrections and to show the differences between two versions of the same text.
Name:Dynamic Programming 2
Description:In this module, we continue practicing implementing dynamic programming solutions.
This online course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).
