About this Course
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Learner Career Outcomes

18%

started a new career after completing these courses

22%

got a tangible career benefit from this course

16%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 35 hours to complete

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

English

Subtitles: English, Korean

Skills you will gain

GraphsData StructureAlgorithmsData Compression

Learner Career Outcomes

18%

started a new career after completing these courses

22%

got a tangible career benefit from this course

16%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 35 hours to complete

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

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Week
1
10 minutes to complete

Introduction

1 video (Total 9 min), 2 readings
1 video
2 readings
Welcome to Algorithms, Part II1m
Lecture Slides
2 hours to complete

Undirected Graphs

6 videos (Total 98 min), 2 readings, 1 quiz
6 videos
Graph API14m
Depth-First Search26m
Breadth-First Search13m
Connected Components18m
Graph Challenges14m
2 readings
Overview1m
Lecture Slides
1 practice exercise
Interview Questions: Undirected Graphs (ungraded)6m
9 hours to complete

Directed Graphs

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

Minimum Spanning Trees

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

Shortest Paths

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

Maximum Flow and Minimum Cut

6 videos (Total 72 min), 2 readings, 2 quizzes
6 videos
Ford–Fulkerson Algorithm6m
Maxflow–Mincut Theorem9m
Running Time Analysis8m
Java Implementation14m
Maxflow Applications22m
2 readings
Overview
Lecture Slides
1 practice exercise
Interview Questions: Maximum Flow (ungraded)6m
2 hours to complete

Radix Sorts

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

Tries

3 videos (Total 75 min), 2 readings, 1 quiz
3 videos
Ternary Search Tries22m
Character-Based Operations20m
2 readings
Overview10m
Lecture Slides
1 practice exercise
Interview Questions: Tries (ungraded)6m
10 hours to complete

Substring Search

5 videos (Total 75 min), 1 reading, 2 quizzes
5 videos
Brute-Force Substring Search10m
Knuth–Morris–Pratt33m
Boyer–Moore8m
Rabin–Karp16m
1 reading
Lecture Slides10m
1 practice exercise
Interview Questions: Substring Search (ungraded)6m
5.0
148 ReviewsChevron Right

Top reviews from Algorithms, Part II

By IOJan 21st 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.

By AKApr 17th 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.

Instructors

Avatar

Robert Sedgewick

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

Kevin Wayne

Phillip Y. Goldman '86 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.

  • Once you enroll, you’ll have access to all videos and programming 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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