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, William O. Baker *39 Professor of Computer Science
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
- Leyendo: Welcome to Algorithms, Part I
- Leyendo: Lecture Slides
- Video: Course Introduction
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.
5 videos, 2 readings, 1 practice quiz
- Leyendo: Overview
- Leyendo: Lecture Slides
- Video: Dynamic Connectivity
- Video: Quick Find
- Video: Quick Union
- Video: Quick-Union Improvements
- Video: Union−Find Applications
- Cuestionario de práctica: Interview Questions: Union–Find (ungraded)
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. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs.
6 videos, 1 reading, 1 practice quiz
- Leyendo: Lecture Slides
- Video: Analysis of Algorithms Introduction
- Video: Observations
- Video: Mathematical Models
- Video: Order-of-Growth Classifications
- Video: Theory of Algorithms
- Video: Memory
- Cuestionario de práctica: Interview Questions: Analysis of Algorithms (ungraded)
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 code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems.
6 videos, 2 readings, 1 practice quiz
- Leyendo: Overview
- Leyendo: Lecture Slides
- Video: Stacks
- Video: Resizing Arrays
- Video: Queues
- Video: Generics
- Video: Iterators
- Video: Stack and Queue Applications (optional)
- Cuestionario de práctica: Interview Questions: Stacks and Queues (ungraded)
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 conclude with an application of sorting to computing the convex hull via the Graham scan algorithm.
6 videos, 1 reading, 1 practice quiz
- Leyendo: Lecture Slides
- Video: Sorting Introduction
- Video: Selection Sort
- Video: Insertion Sort
- Video: Shellsort
- Video: Shuffling
- Video: Convex Hull
- Cuestionario de práctica: Interview Questions: Elementary Sorts (ungraded)
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 the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability.
5 videos, 2 readings, 1 practice quiz
- Leyendo: Overview
- Leyendo: Lecture Slides
- Video: Mergesort
- Video: Bottom-up Mergesort
- Video: Sorting Complexity
- Video: Comparators
- Video: Stability
- Cuestionario de práctica: Interview Questions: Mergesort (ungraded)
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 quicksort that works especially well in the presence of duplicate keys.
4 videos, 1 reading, 1 practice quiz
- Leyendo: Lecture Slides
- Video: Quicksort
- Video: Selection
- Video: Duplicate Keys
- Video: System Sorts
- Cuestionario de práctica: Interview Questions: Quicksort (ungraded)
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 simulate the motion of n particles subject to the laws of elastic collision.
4 videos, 2 readings, 1 practice quiz
- Leyendo: Overview
- Leyendo: Lecture Slides
- Video: APIs and Elementary Implementations
- Video: Binary Heaps
- Video: Heapsort
- Video: Event-Driven Simulation (optional)
- Cuestionario de práctica: Interview Questions: Priority Queues (ungraded)
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 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.
6 videos, 1 reading, 1 practice quiz
- Leyendo: Lecture Slides
- Video: Symbol Table API
- Video: Elementary Implementations
- Video: Ordered Operations
- Video: Binary Search Trees
- Video: Ordered Operations in BSTs
- Video: Deletion in BSTs
- Cuestionario de práctica: Interview Questions: Elementary Symbol Tables (ungraded)
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 search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce B-trees, a generalization of 2−3 trees that are widely used to implement file systems.
3 videos, 2 readings, 1 practice quiz
- Leyendo: Overview
- Leyendo: Lecture Slides
- Video: 2−3 Search Trees
- Video: Red-Black BSTs
- Video: B-Trees (optional)
- Cuestionario de práctica: Interview Questions: Balanced Search Trees (ungraded)
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 consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles.
5 videos, 1 reading
- Leyendo: Lecture Slides
- Video: 1d Range Search
- Video: Line Segment Intersection
- Video: Kd-Trees
- Video: Interval Search Trees
- Video: Rectangle Intersection
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 strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constant-time performance for search and insert under the uniform hashing assumption.
4 videos, 2 readings, 1 practice quiz
- Leyendo: Overview
- Leyendo: Lecture Slides
- Video: Hash Tables
- Video: Separate Chaining
- Video: Linear Probing
- Video: Hash Table Context
- Cuestionario de práctica: Interview Questions: Hash Tables (ungraded)
Symbol Table Applications
We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.
4 videos, 1 reading
- Leyendo: Lecture Slides
- Video: Symbol Table Applications: Sets (optional)
- Video: Symbol Table Applications: Dictionary Clients (optional)
- Video: Symbol Table Applications: Indexing Clients (optional)
- Video: Symbol Table Applications: Sparse Vectors (optional)
FAQs
How It Works
Trabajo del curso
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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
Thank you so much Professor Sedgewick. This is one of the greatest computer science course I've ever taken!
Nice course, interesting topics. Material was presented in a way that I found very clear.
Beautiful course, I am feeling after spending 10 years in programming now I an on right track.
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I work outside of this field. No prior education, interest, foundation. So far so good. Will surely be stumped by parts of this, but along for the ride. I am best educated via imagery at times, especially in discussing concrete subjects. I stopped at calculus I. I didn't do so well.... I tried.... we'll see how this goes. I"m glad this is here.