This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.
課程信息
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高級
Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.
完成時間大約為38 小時
英語（English）
字幕：英語（English）
您將學到的內容有
Describe basic algorithm design techniques
Create divide and conquer, dynamic programming, and greedy algorithms
Understand intractable problems, P vs NP and the use of integer programming solvers to tackle some of these problems
您將獲得的技能
- Analysis of Algorithms
- Algorithm Design
- Python Programming
- Data Structure Design
- Intractability
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。
高級
Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.
完成時間大約為38 小時
英語（English）
字幕：英語（English）
提供方
科罗拉多大学波德分校
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
立即開始攻讀碩士學位
此 課程 隸屬於 科罗拉多大学波德分校 提供的 100% 在線 Master of Science in Data Science。
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授課大綱 - 您將從這門課程中學到什麼
完成時間為 11 小時
Divide and Conquer Algorithms
We will formally cover divide and conquer algorithms as a design scheme and look at some divide and conquer algorithms we have encountered in the past. We will learn some divide and conquer algorithms for Integer Multiplication (Karatsuba’s Algorithm), Matrix Multiplication (Strassen’s Algorithm), Fast Fourier Transforms (FFTs), and Finding Closest Pair of Points.
完成時間為 11 小時
9 個視頻 （總計 231 分鐘）, 10 個閱讀材料, 6 個測驗
完成時間為 9 小時
Dynamic Programming Algorithms
In this module, you will learn about dynamic programming as a design principle for algorithms. We will provide a step-by-step approach to formulating a problem as a dynamic program and solving these problems using memoization. We will cover dynamic programming for finding longest common subsequences, Knapsack problem and some interesting dynamic programming applications.
完成時間為 9 小時
6 個視頻 （總計 131 分鐘）, 6 個閱讀材料, 6 個測驗
完成時間為 7 小時
Greedy Algorithms
In this module, we will learn about greedy algorithms. We will understand the basic design principles for greedy algorithms and learn about a few algorithms for greedy scheduling and Huffman codes. We will also learn some interesting cases when being greedy provides a guaranteed approximation to the actual solution.
完成時間為 7 小時
5 個視頻 （總計 114 分鐘）, 4 個閱讀材料, 4 個測驗
完成時間為 11 小時
Intractability and Supplement on Quantum Computing
P vs NP, Examples such as Travelling Salesperson Problem, Vertex Cover, 3-Coloring and others; Integer Linear Programming and Translating Problems into Integer Programming.
完成時間為 11 小時
9 個視頻 （總計 288 分鐘）, 5 個閱讀材料, 5 個測驗
關於 Data Science Foundations: Data Structures and Algorithms 專項課程
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.
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