About this Course
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100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 33 hours to complete

Suggested: 6 weeks of study, 6–10 hours per week....
Available languages

English

Subtitles: English, Korean

Skills you will gain

GraphsData StructureAlgorithmsData Compression
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 33 hours to complete

Suggested: 6 weeks of study, 6–10 hours per week....
Available languages

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Week
1
Hours to complete
10 minutes to complete

Introduction

Welcome to Algorithms, Part II....
Reading
1 video (Total 9 min), 2 readings
Video1 video
Reading2 readings
Welcome to Algorithms, Part II1m
Lecture Slides0
Hours to complete
2 hours to complete

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....
Reading
6 videos (Total 98 min), 2 readings, 1 quiz
Video6 videos
Graph API14m
Depth-First Search26m
Breadth-First Search13m
Connected Components18m
Graph Challenges14m
Reading2 readings
Overview1m
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Undirected Graphs (ungraded)6m
Hours to complete
4 hours to complete

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....
Reading
5 videos (Total 68 min), 1 reading, 2 quizzes
Video5 videos
Digraph API4m
Digraph Search20m
Topological Sort 12m
Strong Components20m
Reading1 reading
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Directed Graphs (ungraded)6m
Week
2
Hours to complete
2 hours to complete

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....
Reading
6 videos (Total 85 min), 2 readings, 1 quiz
Video6 videos
Greedy Algorithm12m
Edge-Weighted Graph API11m
Kruskal's Algorithm12m
Prim's Algorithm33m
MST Context10m
Reading2 readings
Overview1m
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Minimum Spanning Trees (ungraded)6m
Hours to complete
5 hours to complete

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....
Reading
5 videos (Total 85 min), 1 reading, 2 quizzes
Video5 videos
Shortest Path Properties14m
Dijkstra's Algorithm18m
Edge-Weighted DAGs19m
Negative Weights21m
Reading1 reading
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Shortest Paths (ungraded)6m
Week
3
Hours to complete
4 hours to complete

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....
Reading
6 videos (Total 72 min), 2 readings, 2 quizzes
Video6 videos
Ford–Fulkerson Algorithm6m
Maxflow–Mincut Theorem9m
Running Time Analysis8m
Java Implementation14m
Maxflow Applications22m
Reading2 readings
Overview0
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Maximum Flow (ungraded)6m
Hours to complete
2 hours to complete

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....
Reading
6 videos (Total 85 min), 1 reading, 1 quiz
Video6 videos
Key-Indexed Counting12m
LSD Radix Sort15m
MSD Radix Sort13m
3-way Radix Quicksort7m
Suffix Arrays19m
Reading1 reading
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Radix Sorts (ungraded)6m
Week
4
Hours to complete
2 hours to complete

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....
Reading
3 videos (Total 75 min), 2 readings, 1 quiz
Video3 videos
Ternary Search Tries22m
Character-Based Operations20m
Reading2 readings
Overview10m
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Tries (ungraded)6m
Hours to complete
5 hours to complete

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....
Reading
5 videos (Total 75 min), 1 reading, 2 quizzes
Video5 videos
Brute-Force Substring Search10m
Knuth–Morris–Pratt33m
Boyer–Moore8m
Rabin–Karp16m
Reading1 reading
Lecture Slides10m
Quiz1 practice exercise
Interview Questions: Substring Search (ungraded)6m

Instructors

Avatar

Robert Sedgewick

William O. Baker *39 Professor of Computer Science
Computer Science
Avatar

Kevin Wayne

Senior Lecturer
Computer Science

About 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....

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • No. All features of this course are available for free.

  • No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with 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.

  • 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.

  • 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.

  • 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.

  • 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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