算法思想(第2部分)

Algorithmic Thinking (Part 2)

1545 次查看
莱斯大学
Coursera
  • 完成时间大约为 20 个小时
  • 中级
  • 英语, 韩语, 西班牙语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Algorithms

Python Programming

Algorithmic Efficiency

Dynamic Programming

课程概况

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of “Algorithmic Thinking”, allowing them to build simpler, more efficient solutions to computational problems.

In part 2 of this course, we will study advanced algorithmic techniques such as divide-and-conquer and dynamic programming. As the central part of the course, students will implement several algorithms in Python that incorporate these techniques and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.

Once students have completed this class, they will have both the mathematical and programming skills to analyze, design, and program solutions to a wide range of computational problems. While this class will use Python as its vehicle of choice to practice Algorithmic Thinking, the concepts that you will learn in this class transcend any particular programming language.

课程大纲

周1
完成时间为 3 小时
Module 3 - Core Materials
Sorting, searching, big-O notation, the Master Theorem
13 个视频 (总计 147 分钟), 2 个阅读材料, 1 个测验

周2
完成时间为 5 小时
Module 3 - Project and Application
Closest pairs of points, clustering of points, comparison of clustering algorithms
4 个阅读材料, 3 个测验

周3
完成时间为 2 小时
Module 4 - Core Materials
Dynamic programming, running time of DP algorithms, local and global sequence alignment
7 个视频 (总计 87 分钟), 1 个测验

周4
完成时间为 4 小时
Module 4 - Project and Application
Computation of sequence alignments, applications to genomics and text comparison

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