關於此課程：The 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).
此課程適用人群：Programmers with basic experience looking to understand the practical and conceptual underpinnings of algorithms, with the goal of becoming more effective software engineers. Computer science students and researchers as well as interdisciplinary students (studying electrical engineering, mathematics, bioinformatics, etc.) aiming to get more profound understanding of algorithms and hands-on experience implementing them and applying for real-world problems. Applicants who want to prepare for an interview in a high-tech company. What background knowledge is necessary? 1. Basic knowledge of at least one programming language: C++, Java, Python C, C#,Haskell, Javascript, Kotlin, Ruby, Rust, Scala. We expect you to be able to implement programs that: 1) read data from the standard input (in most cases, the input is a sequence of integers); 2) compute the result (in most cases, a few loops are enough for this); 3) print the result to the standard output. For each programming challenge in this course, we provide starter solutions in C++, Java, and Python. The best way to check whether your programming skills are enough to go through problems in this specialization is to solve two problems from the first week. If you are able to pass them (after reading our tutorials), then you will definitely be able to pass the course. 2. Basic knowledge of discrete mathematics: proof by induction, proof by contradiction. Knowledge of discrete mathematics is necessary for analyzing algorithms (proving correctness, estimating running time) and for algorithmic thinking in general. If you want to refresh your discrete mathematics skills, we encourage you to go through our partner specialization — Introduction to Discrete Mathematics for Computer Science (https://www.coursera.org/specializations/discrete-mathematics). It teaches the basics of discrete mathematics in try-this-before-we-explain-everything approach: you will be solving many interactive puzzles that were carefully designed to allow you to invent many of the important ideas and concepts yourself.
製作方： University of California San Diego, National Research University Higher School of Economics
教學方： Alexander S. Kulikov, Visiting Professor
Department of Computer Science and Engineering
教學方： Michael Levin, Lecturer
Computer Science
教學方： Neil Rhodes, Adjunct Faculty
Computer Science and Engineering
教學方： Pavel Pevzner, Professor
Department of Computer Science and Engineering
教學方： Daniel M Kane, Assistant Professor
Department of Computer Science and Engineering / Department of Mathematics
| 基本信息 | 課程 1（共 6 門，数据结构与算法 Specialization ） |
| 級別 | 中級 |
| 承諾學習時間 | 5 weeks of study, 4-8 hours/week |
| 語言 | English, 字幕：Spanish |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
授課大綱
第 1 週
Programming Challenges
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.
6 個視頻, 4 個閱讀材料, 1 個練習測驗
- Lecture: Companion MOOCBook
- Lecture: What background knowledge is necessary?
- Quiz pour s'exercer: Solving Programming Challenges
- Lecture: Optional Videos and Screencasts
- 視頻: Solving the Sum of Two Digits Programming Challenges (screencast)
- 視頻: Solving the Maximum Pairwise Product Programming Challenge: Improving the Naive Solution, Testing, Debugging
- 視頻: Stress Test - Implementation
- 視頻: Stress Test - Find the Test and Debug
- 視頻: Stress Test - More Testing, Submit and Pass!
- Lecture: Acknowledgements
已評分: Programming Assignment 1: Programming Challenges
第 2 週
Algorithmic Warm-up
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!
12 個視頻, 3 個閱讀材料, 3 個練習測驗
- 視頻: Coming Up
- 視頻: Problem Overview
- 視頻: Naive Algorithm
- 視頻: Efficient Algorithm
- Lecture: Resources
- 視頻: Problem Overview and Naive Algorithm
- 視頻: Efficient Algorithm
- Lecture: Resources
- 視頻: Computing Runtimes
- 視頻: Asymptotic Notation
- 視頻: Big-O Notation
- 視頻: Using Big-O
- Lecture: Resources
- Quiz pour s'exercer: Logarithms
- Quiz pour s'exercer: Big-O
- Quiz pour s'exercer: Growth rate
- 視頻: Course Overview
已評分: Programming Assignment 2: Algorithmic Warm-up
第 3 週
Greedy Algorithms
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.
10 個視頻, 1 個閱讀材料, 2 個練習測驗
- 視頻: Car Fueling
- 視頻: Car Fueling - Implementation and Analysis
- 視頻: Main Ingredients of Greedy Algorithms
- Quiz pour s'exercer: Greedy Algorithms
- 視頻: Celebration Party Problem
- 視頻: Efficient Algorithm for Grouping Children
- 視頻: Analysis and Implementation of the Efficient Algorithm
- 視頻: Long Hike
- 視頻: Fractional Knapsack - Implementation, Analysis and Optimization
- 視頻: Review of Greedy Algorithms
- Lecture: Resources
- Quiz pour s'exercer: Fractional Knapsack
已評分: Programming Assignment 3: Greedy Algorithms
第 4 週
Divide-and-Conquer
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!
20 個視頻, 5 個閱讀材料, 5 個練習測驗
- 視頻: Linear Search
- 視頻: Binary Search
- 視頻: Binary Search Runtime
- Lecture: Resources
- Quiz pour s'exercer: Linear Search and Binary Search
- 視頻: Problem Overview and Naïve Solution
- 視頻: Naïve Divide and Conquer Algorithm
- 視頻: Faster Divide and Conquer Algorithm
- Lecture: Resources
- Quiz pour s'exercer: Polynomial Multiplication
- 視頻: What is the Master Theorem?
- 視頻: Proof of the Master Theorem
- Lecture: Resources
- Quiz pour s'exercer: Master Theorem
- 視頻: Problem Overview
- 視頻: Selection Sort
- 視頻: Merge Sort
- 視頻: Lower Bound for Comparison Based Sorting
- 視頻: Non-Comparison Based Sorting Algorithms
- Lecture: Resources
- Quiz pour s'exercer: Sorting
- 視頻: Overview
- 視頻: Algorithm
- 視頻: Random Pivot
- 視頻: Running Time Analysis (optional)
- 視頻: Equal Elements
- 視頻: Final Remarks
- Lecture: Resources
- Quiz pour s'exercer: Quick Sort
已評分: Programming Assignment 4: Divide and Conquer
第 5 週
Dynamic Programming 1
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.
4 個視頻, 2 個閱讀材料, 2 個練習測驗
- Quiz pour s'exercer: Change Money
- Lecture: Resources
- 視頻: The Alignment Game
- 視頻: Computing Edit Distance
- 視頻: Reconstructing an Optimal Alignment
- Quiz pour s'exercer: Edit Distance
- Lecture: Resources
已評分: Programming Assignment 5: Dynamic Programming 1
第 6 週
Dynamic Programming 2
In this module, we continue practicing implementing dynamic programming solutions.
8 個視頻, 1 個閱讀材料, 2 個練習測驗
- Quiz pour s'exercer: Knapsack
- 視頻: Knapsack with Repetitions
- 視頻: Knapsack without Repetitions
- 視頻: Final Remarks
- Lecture: Resources
- 視頻: Problem Overview
- Quiz pour s'exercer: Maximum Value of an Arithmetic Expression
- 視頻: Subproblems
- 視頻: Algorithm
- 視頻: Reconstructing a Solution
已評分: Programming Assignment 6: Dynamic Programming 2
常見問題解答
運作方式
Travail en cours
Chaque cours fonctionne comme un manuel interactif en proposant des vidéos préenregistrées, des quiz et des projets.
Aide de la part de vos pairs
Connectez-vous à des milliers d'autres étudiants et débattez sur des idées, discutez le contenu du cours et obtenez de l'aide pour en maîtriser les concepts.
Certificats
Obtenez une reconnaissance officielle pour votre travail et partagez votre réussite avec vos amis, vos collègues et vos employeurs.
製作方
University of California San Diego
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.
National Research University Higher School of Economics
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.
Learn more on www.hse.ru
價格
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
評分和審閱
This course was really helping for me as i got to learn a lot from it.
The greatness of this course can be judged by the kind of programming assignments given at the end which actually test the learners. Highly recommend this course for people who are totally new to algorithms and want to kickstart their coding venture.
It was difficult but somewhat easy to understand,
even though I didn't have enough knowledge of mathematics to start.
PK
It's so tough.