This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
All the features of this course are available for free. It does not offer a certificate upon completion.
Algorithms, Part II
proposé par
Princeton University
À propos de ce cours
5.0
565 notes
Compétences que vous acquerrez
GraphsData StructureAlgorithmsData Compression
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Introduction
Welcome to Algorithms, Part II.
1 vidéo (Total 9 min), 2 lectures
1 vidéo
Course Introduction9 min
2 lectures
Welcome to Algorithms, Part II1 min
Lecture Slides
Undirected Graphs
We define an undirected graph API and consider the adjacency-matrix and adjacency-lists representations. We introduce two classic algorithms for searching a graph—depth-first search and breadth-first search. We also consider the problem of computing connected components and conclude with related problems and applications.
6 vidéos (Total 98 min), 2 lectures, 1 quiz
6 vidéos
Graph API14 min
Depth-First Search26 min
Breadth-First Search13 min
Connected Components18 min
Graph Challenges14 min
2 lectures
Overview1 min
Lecture Slides
1 exercice pour s'entraîner
Interview Questions: Undirected Graphs (ungraded)6 min
Directed Graphs
In this lecture we study directed graphs. We begin with depth-first search and breadth-first search in digraphs and describe applications ranging from garbage collection to web crawling. Next, we introduce a depth-first search based algorithm for computing the topological order of an acyclic digraph. Finally, we implement the Kosaraju−Sharir algorithm for computing the strong components of a digraph.
5 vidéos (Total 68 min), 1 lecture, 2 quiz
5 vidéos
Digraph API4 min
Digraph Search20 min
Topological Sort 12 min
Strong Components20 min
1 lecture
Lecture Slides
1 exercice pour s'entraîner
Interview Questions: Directed Graphs (ungraded)6 min
Semaine
2Minimum Spanning Trees
In this lecture we study the minimum spanning tree problem. We begin by considering a generic greedy algorithm for the problem. Next, we consider and implement two classic algorithm for the problem—Kruskal's algorithm and Prim's algorithm. We conclude with some applications and open problems.
6 vidéos (Total 85 min), 2 lectures, 1 quiz
6 vidéos
Introduction to MSTs4 min
Greedy Algorithm12 min
Edge-Weighted Graph API11 min
Kruskal's Algorithm12 min
Prim's Algorithm33 min
MST Context10 min
2 lectures
Overview1 min
Lecture Slides
1 exercice pour s'entraîner
Interview Questions: Minimum Spanning Trees (ungraded)6 min
Shortest Paths
In this lecture we study shortest-paths problems. We begin by analyzing some basic properties of shortest paths and a generic algorithm for the problem. We introduce and analyze Dijkstra's algorithm for shortest-paths problems with nonnegative weights. Next, we consider an even faster algorithm for DAGs, which works even if the weights are negative. We conclude with the Bellman−Ford−Moore algorithm for edge-weighted digraphs with no negative cycles. We also consider applications ranging from content-aware fill to arbitrage.
5 vidéos (Total 85 min), 1 lecture, 2 quiz
5 vidéos
Shortest Paths APIs10 min
Shortest Path Properties14 min
Dijkstra's Algorithm18 min
Edge-Weighted DAGs19 min
Negative Weights21 min
1 lecture
Lecture Slides
1 exercice pour s'entraîner
Interview Questions: Shortest Paths (ungraded)6 min
Semaine
3Maximum Flow and Minimum Cut
In this lecture we introduce the maximum flow and minimum cut problems. We begin with the Ford−Fulkerson algorithm. To analyze its correctness, we establish the maxflow−mincut theorem. Next, we consider an efficient implementation of the Ford−Fulkerson algorithm, using the shortest augmenting path rule. Finally, we consider applications, including bipartite matching and baseball elimination.
6 vidéos (Total 72 min), 2 lectures, 2 quiz
6 vidéos
Introduction to Maxflow10 min
Ford–Fulkerson Algorithm6 min
Maxflow–Mincut Theorem9 min
Running Time Analysis8 min
Java Implementation14 min
Maxflow Applications22 min
2 lectures
Overview
Lecture Slides
1 exercice pour s'entraîner
Interview Questions: Maximum Flow (ungraded)6 min
Radix Sorts
In this lecture we consider specialized sorting algorithms for strings and related objects. We begin with a subroutine to sort integers in a small range. We then consider two classic radix sorting algorithms—LSD and MSD radix sorts. Next, we consider an especially efficient variant, which is a hybrid of MSD radix sort and quicksort known as 3-way radix quicksort. We conclude with suffix sorting and related applications.
6 vidéos (Total 85 min), 1 lecture, 1 quiz
6 vidéos
Strings in Java17 min
Key-Indexed Counting12 min
LSD Radix Sort15 min
MSD Radix Sort13 min
3-way Radix Quicksort7 min
Suffix Arrays19 min
1 lecture
Lecture Slides
1 exercice pour s'entraîner
Interview Questions: Radix Sorts (ungraded)6 min
Semaine
4Tries
In this lecture we consider specialized algorithms for symbol tables with string keys. Our goal is a data structure that is as fast as hashing and even more flexible than binary search trees. We begin with multiway tries; next we consider ternary search tries. Finally, we consider character-based operations, including prefix match and longest prefix, and related applications.
3 vidéos (Total 75 min), 2 lectures, 1 quiz
2 lectures
Overview10 min
Lecture Slides
1 exercice pour s'entraîner
Interview Questions: Tries (ungraded)6 min
Substring Search
In this lecture we consider algorithms for searching for a substring in a piece of text. We begin with a brute-force algorithm, whose running time is quadratic in the worst case. Next, we consider the ingenious Knuth−Morris−Pratt algorithm whose running time is guaranteed to be linear in the worst case. Then, we introduce the Boyer−Moore algorithm, whose running time is sublinear on typical inputs. Finally, we consider the Rabin−Karp fingerprint algorithm, which uses hashing in a clever way to solve the substring search and related problems.
5 vidéos (Total 75 min), 1 lecture, 2 quiz
5 vidéos
Brute-Force Substring Search10 min
Knuth–Morris–Pratt33 min
Boyer–Moore8 min
Rabin–Karp16 min
1 lecture
Lecture Slides10 min
1 exercice pour s'entraîner
Interview Questions: Substring Search (ungraded)6 min
Enseignants
Robert Sedgewick
William O. Baker *39 Professor of Computer ScienceComputer Science
Kevin Wayne
Senior LecturerComputer Science
À propos de Princeton University
Princeton University is a private research university located in Princeton, New Jersey, United States. It is one of the eight universities of the Ivy League, and one of the nine Colonial Colleges founded before the American Revolution.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si j'achète le Certificat ?
Lorsque vous achetez un Certificat, vous bénéficiez d'un accès à tout le contenu du cours, y compris les devoirs notés. Lorsque vous avez terminé et réussi le cours, votre Certificat électronique est ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
Quelle est la politique de remboursement ?
Une aide financière est-elle possible ?
Our central thesis is that algorithms are best understood by implementing and testing them. Our use of Java is essentially expository, and we shy away from exotic language features, so we expect you would be able to adapt our code to your favorite language. However, we require that you submit the programming assignments in Java.
Which algorithms and data structures are covered in this course?
Part II focuses on graph and string-processing algorithms. Topics include depth-first search, breadth-first search, topological sort, Kosaraju−Sharir, Kruskal, Prim, Dijkistra, Bellman−Ford, Ford−Fulkerson, LSD radix sort, MSD radix sort, 3-way radix quicksort, multiway tries, ternary search tries, Knuth−Morris−Pratt, Boyer−Moore, Rabin−Karp, regular expression matching, run-length coding, Huffman coding, LZW compression, and the Burrows−Wheeler transform.
Part I focuses on elementary data structures, sorting, and searching. Topics include union-find, binary search, stacks, queues, bags, insertion sort, selection sort, shellsort, quicksort, 3-way quicksort, mergesort, heapsort, binary heaps, binary search trees, red−black trees, separate-chaining and linear-probing hash tables, Graham scan, and kd-trees.
What kinds of assessments are available in this course?
Weekly programming assignments and interview questions.
The programming assignments involve either implementing algorithms and data structures (graph algorithms, tries, and the Burrows–Wheeler transform) or applying algorithms and data structures to an interesting domain (computer graphics, computational linguistics, and data compression). The assignments are evaluated using a sophisticated autograder that provides detailed feedback about style, correctness, and efficiency.
The interview questions are similar to those that you might find at a technical job interview. They are optional and not graded.
I am/was not a Computer Science major. Is this course for me?
This course is for anyone using a computer to address large problems (and therefore needing efficient algorithms). At Princeton, over 25% of all students take the course, including people majoring in engineering, biology, physics, chemistry, economics, and many other fields, not just computer science.
How does this course differ from Design and Analysis of Algorithms?
The two courses are complementary. This one is essentially a programming course that concentrates on developing code; that one is essentially a math course that concentrates on understanding proofs. This course is about learning algorithms in the context of implementing and testing them in practical applications; that one is about learning algorithms in the context of developing mathematical models that help explain why they are efficient. In typical computer science curriculums, a course like this one is taken by first- and second-year students and a course like that one is taken by juniors and seniors.
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