About this course: 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.
Created by: Princeton University
Taught by: Kevin Wayne, Senior Lecturer
Computer Science
Taught by: Robert Sedgewick, Professor
Computer Science
| Level | Intermediate |
| Commitment | 6 weeks of study, 6–10 hours per week. |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Course Introduction
Welcome to Algorithms, Part I.
1 video, 2 readings
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 qui...
5 videos, 2 readings, 1 practice quiz
Graded: Percolation
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. Ne...
6 videos, 1 reading, 1 practice quiz
WEEK 2
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 cod...
6 videos, 2 readings, 1 practice quiz
Graded: Deques and Randomized Queues
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 concl...
6 videos, 1 reading, 1 practice quiz
WEEK 3
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...
5 videos, 2 readings, 1 practice quiz
Graded: Collinear Points
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 quickso...
4 videos, 1 reading, 1 practice quiz
WEEK 4
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 sim...
4 videos, 2 readings, 1 practice quiz
Graded: 8 Puzzle
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 ...
6 videos, 1 reading, 1 practice quiz
WEEK 5
Balanced Search Trees
In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2−3 trees, which are easy to analyze but hard to implement. Next, we consider red−black binary searc...
3 videos, 2 readings, 1 practice quiz
Geometric Applications of BSTs
We start with 1d and 2d range searching, where the goal is to find all points in a given 1d or 2d interval. To accomplish this, we consider kd-trees, a natural generalization of BSTs when the keys are points in the plane (or higher dimensions). We also conside...
5 videos, 1 reading
Graded: Kd-Trees
WEEK 6
Hash Tables
We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strat...
4 videos, 2 readings, 1 practice quiz
Symbol Table Applications
We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.
4 videos, 1 reading
FAQs
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Coursework
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Creators
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.
Ratings and Reviews
the course was perfect after the introductory CS course, it make you starting to pay more attention to how efficient you program runs. Besides, aside from the intricate math analysis, the instructor also spent a fair amount of time emphasizing on the practical applications where these algorithm truly shines, I love the course not only because the knowledge I acquire about these alogorithm, but the more fundamental reason why we even need them at the first place.
HW each week was fun and challenging, however, sometimes it requires a fair amount of self-exploring before hitting the right track, but it was totally worth it.
I really enjoyed it.
Very well structured, lots of applications, very comprehensive feedback for projects.
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Great course! Really helpful in understanding concepts in algorithms and data structures.
! Just nice! specially thanks Sir Sedgewick.