About this Course
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4,298 ratings
932 reviews
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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. 31 hours to complete

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

English

Subtitles: English, Korean

Skills you will gain

Data StructurePriority QueueAlgorithmsJava Programming
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. 31 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

Course Introduction

Welcome to Algorithms, Part I....
Reading
1 video (Total 9 min), 2 readings
Video1 video
Reading2 readings
Welcome to Algorithms, Part I1m
Lecture Slides0
Hours to complete
6 hours to complete

Union−Find

We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the union−find data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the union−find data type to the percolation problem from physical chemistry....
Reading
5 videos (Total 51 min), 2 readings, 2 quizzes
Video5 videos
Quick Find10m
Quick Union7m
Quick-Union Improvements13m
Union−Find Applications9m
Reading2 readings
Overview1m
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Union–Find (ungraded)0
Hours to complete
1 hour to complete

Analysis of Algorithms

The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs....
Reading
6 videos (Total 66 min), 1 reading, 1 quiz
Video6 videos
Observations10m
Mathematical Models12m
Order-of-Growth Classifications14m
Theory of Algorithms11m
Memory8m
Reading1 reading
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Analysis of Algorithms (ungraded)0
Week
2
Hours to complete
6 hours to complete

Stacks and Queues

We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array. We introduce two advanced Java features—generics and iterators—that simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems....
Reading
6 videos (Total 61 min), 2 readings, 2 quizzes
Video6 videos
Resizing Arrays9m
Queues4m
Generics9m
Iterators7m
Stack and Queue Applications (optional)13m
Reading2 readings
Overview1m
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Stacks and Queues (ungraded)0
Hours to complete
1 hour to complete

Elementary Sorts

We introduce the sorting problem and Java's Comparable interface. We study two elementary sorting methods (selection sort and insertion sort) and a variation of one of them (shellsort). We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm....
Reading
6 videos (Total 63 min), 1 reading, 1 quiz
Video6 videos
Selection Sort6m
Insertion Sort9m
Shellsort10m
Shuffling7m
Convex Hull13m
Reading1 reading
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Elementary Sorts (ungraded)0
Week
3
Hours to complete
6 hours to complete

Mergesort

We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottom-up version. We prove that any compare-based sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability....
Reading
5 videos (Total 49 min), 2 readings, 2 quizzes
Video5 videos
Bottom-up Mergesort3m
Sorting Complexity9m
Comparators6m
Stability5m
Reading2 readings
Overview0
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Mergesort (ungraded)0
Hours to complete
1 hour to complete

Quicksort

We introduce and implement the randomized quicksort algorithm and analyze its performance. We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3-way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys....
Reading
4 videos (Total 50 min), 1 reading, 1 quiz
Video4 videos
Selection7m
Duplicate Keys11m
System Sorts11m
Reading1 reading
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Quicksort (ungraded)0
Week
4
Hours to complete
6 hours to complete

Priority Queues

We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision. ...
Reading
4 videos (Total 74 min), 2 readings, 2 quizzes
Video4 videos
Binary Heaps23m
Heapsort14m
Event-Driven Simulation (optional)22m
Reading2 readings
Overview10m
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Priority Queues (ungraded)0
Hours to complete
1 hour to complete

Elementary Symbol Tables

We define an API for symbol tables (also known as associative arrays, maps, or dictionaries) and describe two elementary implementations using a sorted array (binary search) and an unordered list (sequential search). When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance....
Reading
6 videos (Total 77 min), 1 reading, 1 quiz
Video6 videos
Elementary Implementations9m
Ordered Operations6m
Binary Search Trees19m
Ordered Operations in BSTs10m
Deletion in BSTs9m
Reading1 reading
Lecture Slides0
Quiz1 practice exercise
Interview Questions: Elementary Symbol Tables (ungraded)8m

Instructors

Avatar

Kevin Wayne

Senior Lecturer
Computer Science
Avatar

Robert Sedgewick

William O. Baker *39 Professor of Computer Science
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 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.

    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.

  • Weekly exercises, weekly programming assignments, weekly interview questions, and a final exam.

    The exercises are primarily composed of short drill questions (such as tracing the execution of an algorithm or data structure), designed to help you master the material.

    The programming assignments involve either implementing algorithms and data structures (deques, randomized queues, and kd-trees) or applying algorithms and data structures to an interesting domain (computational chemistry, computational geometry, and mathematical recreation). 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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