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.
Ce cours fait partie de la Spécialisation Data Science Foundations: Data Structures and Algorithms
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Spécialisation Data Science Foundations: Data Structures and AlgorithmsUniversité du Colorado à BoulderÀ propos de ce cours
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Cours 3 sur 3 dans le
Niveau avancé
Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.
Approx. 38 heures pour terminer
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Sous-titres : Anglais
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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
Compétences que vous acquerrez
- Analysis of Algorithms
- Algorithm Design
- Python Programming
- Data Structure Design
- Intractability
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Niveau avancé
Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.
Approx. 38 heures pour terminer
Anglais
Sous-titres : Anglais
Votre entreprise pourrait-elle bénéficier de la formation des employés à des compétences recherchées ?
Essayez Coursera pour les affaires
Offert par
Université du Colorado à Boulder
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.
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Ce cours fait partie du diplôme intégralement en ligne Master of Science in Data Science de Université du Colorado à Boulder.
Si vous êtes admis(e) au programme complet, vos cours sont pris en compte dans votre apprentissage diplômant.
Programme de cours : ce que vous apprendrez dans ce cours
11 heures pour terminer
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.
11 heures pour terminer
9 vidéos (Total 231 min), 11 lectures, 6 quiz
9 heures pour terminer
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.
9 heures pour terminer
6 vidéos (Total 131 min), 6 lectures, 6 quiz
7 heures pour terminer
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.
7 heures pour terminer
5 vidéos (Total 102 min), 4 lectures, 4 quiz
11 heures pour terminer
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.
11 heures pour terminer
9 vidéos (Total 288 min), 5 lectures, 5 quiz
Avis
- 5 stars70,58 %
- 4 stars17,64 %
- 3 stars2,94 %
- 2 stars2,94 %
- 1 star5,88 %
Meilleurs avis pour DYNAMIC PROGRAMMING, GREEDY ALGORITHMS
par DM20 sept. 2021
Excellent. This course covers some difficult topics, but the lectures and homework assignments were superb and made them quite approachable.
par AM18 sept. 2022
Great work from professor Sriram Sankaranarayanan explaining such complex material. I wish we could review more examples during the class (specially Dynamic Programming ones).
par YS22 juil. 2022
Excellent course! I really learned alot and enjoyed all the challenges and topics in your course. Thank you so much!
À propos du Spécialisation Data Science Foundations: Data Structures and Algorithms
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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