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.
Algorithms, Part II
Princeton UniversityÜber diesen Kurs
241,361 kürzliche Aufrufe
Karriereergebnisse der Lernenden
18%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
22%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
16%
erhalten Sie eine Gehaltserhöhung oder Beförderung
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 58 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch
Kompetenzen, die Sie erwerben
GraphsData StructureAlgorithmsData Compression
Karriereergebnisse der Lernenden
18%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
22%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
16%
erhalten Sie eine Gehaltserhöhung oder Beförderung
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 58 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch
von
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
10 Minuten zum Abschließen
Introduction
Welcome to Algorithms, Part II.
10 Minuten zum Abschließen
1 Video (Gesamt 9 min), 2 Lektüren
1 Video
2 Lektüren
Welcome to Algorithms, Part II1m
Lecture Slides
2 Stunden zum Abschließen
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.
2 Stunden zum Abschließen
6 Videos (Gesamt 98 min), 2 Lektüren, 1 Quiz
6 Videos
Graph API14m
Depth-First Search26m
Breadth-First Search13m
Connected Components18m
Graph Challenges14m
2 Lektüren
Overview1m
Lecture Slides
1 praktische Übung
Interview Questions: Undirected Graphs (ungraded)6m
9 Stunden zum Abschließen
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.
9 Stunden zum Abschließen
5 Videos (Gesamt 68 min), 1 Lektüre, 2 Quiz
5 Videos
Digraph API4m
Digraph Search20m
Topological Sort 12m
Strong Components20m
1 Lektüre
Lecture Slides
1 praktische Übung
Interview Questions: Directed Graphs (ungraded)6m
2 Stunden zum Abschließen
Minimum 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.
2 Stunden zum Abschließen
6 Videos (Gesamt 85 min), 2 Lektüren, 1 Quiz
6 Videos
Greedy Algorithm12m
Edge-Weighted Graph API11m
Kruskal's Algorithm12m
Prim's Algorithm33m
MST Context10m
2 Lektüren
Overview1m
Lecture Slides
1 praktische Übung
Interview Questions: Minimum Spanning Trees (ungraded)6m
10 Stunden zum Abschließen
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.
10 Stunden zum Abschließen
5 Videos (Gesamt 85 min), 1 Lektüre, 2 Quiz
5 Videos
Shortest Path Properties14m
Dijkstra's Algorithm18m
Edge-Weighted DAGs19m
Negative Weights21m
1 Lektüre
Lecture Slides
1 praktische Übung
Interview Questions: Shortest Paths (ungraded)6m
7 Stunden zum Abschließen
Maximum 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.
7 Stunden zum Abschließen
6 Videos (Gesamt 72 min), 2 Lektüren, 2 Quiz
6 Videos
Ford–Fulkerson Algorithm6m
Maxflow–Mincut Theorem9m
Running Time Analysis8m
Java Implementation14m
Maxflow Applications22m
2 Lektüren
Overview
Lecture Slides
1 praktische Übung
Interview Questions: Maximum Flow (ungraded)6m
2 Stunden zum Abschließen
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.
2 Stunden zum Abschließen
6 Videos (Gesamt 85 min), 1 Lektüre, 1 Quiz
6 Videos
Key-Indexed Counting12m
LSD Radix Sort15m
MSD Radix Sort13m
3-way Radix Quicksort7m
Suffix Arrays19m
1 Lektüre
Lecture Slides
1 praktische Übung
Interview Questions: Radix Sorts (ungraded)6m
2 Stunden zum Abschließen
Tries
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.
2 Stunden zum Abschließen
3 Videos (Gesamt 75 min), 2 Lektüren, 1 Quiz
2 Lektüren
Overview10m
Lecture Slides
1 praktische Übung
Interview Questions: Tries (ungraded)6m
10 Stunden zum Abschließen
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.
10 Stunden zum Abschließen
5 Videos (Gesamt 75 min), 1 Lektüre, 2 Quiz
5 Videos
Brute-Force Substring Search10m
Knuth–Morris–Pratt33m
Boyer–Moore8m
Rabin–Karp16m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Interview Questions: Substring Search (ungraded)6m
Bewertungen
Top-Bewertungen von ALGORITHMS, PART II
von IO20. Jan. 2018
Pretty challenging course, but very good. Having a book is a must (at least it was for me), video lectures complement book nicely, and some topics are explained better in the Algorithms, 4th ed. book.
von AK16. Apr. 2019
Amazing course! Loved the theory and exercises! Just a note for others: Its part 1 had almost no dependency on book, but this part 2 has some dependency (e.g. chapter on Graph) on book as well.
von HT7. Feb. 2020
The algorithms are more difficult than part I, nevertheless Sedgewick's vids are still easy to understand. The only drawback maybe chapter 3, max flow min cut part, which is not very clarified.
von RK25. Sep. 2018
An incredible course that covers a lot of vital algorithm on graphs and strings. I learned a lot of new material that I hadn't known before. Thank you very much for this amazing course!
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Der Zugang zu Vorlesungen und Aufgaben hängt von der Art Ihrer Anmeldung ab. Wenn Sie einen Kurs im Gastmodus belegen, können Sie die meisten Kursmaterialien kostenlos einsehen. Um auf benotete Aufgaben zuzugreifen und ein Zertifikat zu erhalten, müssen Sie während oder nach Ihrer Gastphase das Zertifikat erwerben. Wenn Sie die Gastoption nicht sehen:
- Der Kurs bietet möglicherweise keine Gastoption an. Sie können stattdessen eine kostenlose Testversion ausprobieren oder finanzielle Unterstützung beantragen.
- Der Kurs kann stattdessen "Vollständiger Kurs ohne Zertifikat" anbieten. Mit dieser Option können Sie alle Kursmaterialien einsehen, die erforderlichen Aufgaben einreichen und eine Endnote erhalten. Dies bedeutet auch, dass Sie kein Zertifikat erwerben können.
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Once you enroll, you’ll have access to all videos and programming assignments.
Do I need to pay for this course?
No. All features of this course are available for free.
Can I earn a certificate in this course?
No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with this course.
I have no familiarity with Java programming. Can I still take this course?
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.
Erhalte ich akademische Leistungspunkte für den Abschluss des Kurses?
Für diesen Kurs gibt es keine akademischen Leistungspunkte, doch Hochschulen können nach eigenem Ermessen Leistungspunkte für Kurszertifikate vergeben. Wenden Sie sich an Ihre Einrichtung, um mehr zu erfahren. Online-Abschlüsse und Mastertrack™-Zertifikate auf Coursera bieten die Möglichkeit, akademische Leistungspunkte zu erwerben.
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