À propos de ce cours : 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).
Les destinataires de ce cours : 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.
Créé par : University of California San Diego, National Research University Higher School of Economics
Enseigné par : Alexander S. Kulikov, Visiting Professor
Department of Computer Science and EngineeringEnseigné par : Michael Levin, Lecturer
Computer ScienceEnseigné par : Neil Rhodes, Adjunct Faculty
Computer Science and EngineeringEnseigné par : Pavel Pevzner, Professor
Department of Computer Science and EngineeringEnseigné par : Daniel M Kane, Assistant Professor
Department of Computer Science and Engineering / Department of Mathematics
| Informations de base | Cours 1 de 6 dans Data Structures and Algorithms Specialization |
| Niveau | Intermédiaire |
| Engagement | 5 weeks of study, 4-8 hours/week |
| Langue | English, Sous-titres : Spanish |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
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 vidéos, 4 lectures, 1 quiz pour s'exercer
- Reading: Companion MOOCBook
- Reading: What background knowledge is necessary?
- Practice Quiz: Solving Programming Challenges
- Reading: Optional Videos and Screencasts
- Vidéo: Solving the Sum of Two Digits Programming Challenges (screencast)
- Vidéo: Solving the Maximum Pairwise Product Programming Challenge: Improving the Naive Solution, Testing, Debugging
- Vidéo: Stress Test - Implementation
- Vidéo: Stress Test - Find the Test and Debug
- Vidéo: Stress Test - More Testing, Submit and Pass!
- Reading: Acknowledgements
Noté: Programming Assignment 1: Programming Challenges
SEMAINE 2
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 vidéos, 3 lectures, 3 quiz pour s'exercer
- Vidéo: Coming Up
- Vidéo: Problem Overview
- Vidéo: Naive Algorithm
- Vidéo: Efficient Algorithm
- Reading: Resources
- Vidéo: Problem Overview and Naive Algorithm
- Vidéo: Efficient Algorithm
- Reading: Resources
- Vidéo: Computing Runtimes
- Vidéo: Asymptotic Notation
- Vidéo: Big-O Notation
- Vidéo: Using Big-O
- Reading: Resources
- Practice Quiz: Logarithms
- Practice Quiz: Big-O
- Practice Quiz: Growth rate
- Vidéo: Course Overview
Noté: Programming Assignment 2: Algorithmic Warm-up
SEMAINE 3
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 vidéos, 1 lecture, 2 quiz pour s'exercer
- Vidéo: Car Fueling
- Vidéo: Car Fueling - Implementation and Analysis
- Vidéo: Main Ingredients of Greedy Algorithms
- Practice Quiz: Greedy Algorithms
- Vidéo: Celebration Party Problem
- Vidéo: Efficient Algorithm for Grouping Children
- Vidéo: Analysis and Implementation of the Efficient Algorithm
- Vidéo: Long Hike
- Vidéo: Fractional Knapsack - Implementation, Analysis and Optimization
- Vidéo: Review of Greedy Algorithms
- Reading: Resources
- Practice Quiz: Fractional Knapsack
Noté: Programming Assignment 3: Greedy Algorithms
SEMAINE 4
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 vidéos, 5 lectures, 5 quiz pour s'exercer
- Vidéo: Linear Search
- Vidéo: Binary Search
- Vidéo: Binary Search Runtime
- Reading: Resources
- Practice Quiz: Linear Search and Binary Search
- Vidéo: Problem Overview and Naïve Solution
- Vidéo: Naïve Divide and Conquer Algorithm
- Vidéo: Faster Divide and Conquer Algorithm
- Reading: Resources
- Practice Quiz: Polynomial Multiplication
- Vidéo: What is the Master Theorem?
- Vidéo: Proof of the Master Theorem
- Reading: Resources
- Practice Quiz: Master Theorem
- Vidéo: Problem Overview
- Vidéo: Selection Sort
- Vidéo: Merge Sort
- Vidéo: Lower Bound for Comparison Based Sorting
- Vidéo: Non-Comparison Based Sorting Algorithms
- Reading: Resources
- Practice Quiz: Sorting
- Vidéo: Overview
- Vidéo: Algorithm
- Vidéo: Random Pivot
- Vidéo: Running Time Analysis (optional)
- Vidéo: Equal Elements
- Vidéo: Final Remarks
- Reading: Resources
- Practice Quiz: Quick Sort
Noté: Programming Assignment 4: Divide and Conquer
SEMAINE 5
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 vidéos, 2 lectures, 2 quiz pour s'exercer
- Practice Quiz: Change Money
- Reading: Resources
- Vidéo: The Alignment Game
- Vidéo: Computing Edit Distance
- Vidéo: Reconstructing an Optimal Alignment
- Practice Quiz: Edit Distance
- Reading: Resources
Noté: Programming Assignment 5: Dynamic Programming 1
SEMAINE 6
Dynamic Programming 2
In this module, we continue practicing implementing dynamic programming solutions.
8 vidéos, 1 lecture, 2 quiz pour s'exercer
- Practice Quiz: Knapsack
- Vidéo: Knapsack with Repetitions
- Vidéo: Knapsack without Repetitions
- Vidéo: Final Remarks
- Reading: Resources
- Vidéo: Problem Overview
- Practice Quiz: Maximum Value of an Arithmetic Expression
- Vidéo: Subproblems
- Vidéo: Algorithm
- Vidéo: Reconstructing a Solution
Noté: Programming Assignment 6: Dynamic Programming 2
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Créateurs
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
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
Excellent course with some great coding challenges
Incredible course covers all the basic content that gave in the university in the first semester in a good way.
Got very hard at the end
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O
A tough course no doubt, but you learn all the classic problem solving techniques in computer science. Well worth the effort.