Algorithms, Part I

Course
en
English
Subtitles available
60 h
This content is rated 0 out of 5
Source
  • From www.coursera.org
Conditions
  • Self-paced
  • Free Access
  • Fee-based Certificate
More info
  • 6 Sequences
  • Intermediate Level
  • Subtitles in Korean

Their employees are learning daily with Edflex

  • Safran
  • Air France
  • TotalEnergies
  • Generali
Learn more

Course details

Syllabus

  • Week 1 - Course Introduction
    Welcome to Algorithms, Part I.
  • Week 1 - 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...
  • Week 1 - 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...
  • 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...
  • Week 2 - 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...
  • 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...
  • Week 3 - 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...
  • 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...
  • Week 4 - 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 ...
  • 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...
  • Week 5 - 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...
  • 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...
  • Week 6 - Symbol Table Applications
    We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.

Prerequisite

None.

Instructors

Kevin Wayne
Senior Lecturer
Computer Science

Robert Sedgewick
William O. Baker *39 Professor of Computer Science
Computer Science

Editor

Princeton University, also known as Princeton, is a private American university located in the town of Princeton, New Jersey, in the United States. Founded in 1746, it is the fourth oldest institution of higher education in the United States.

Ranked among the top universities in the world in most international rankings, it enjoys great prestige1. It is a member of the Ivy League, where it has a historic rivalry with Harvard University and Yale University2.

It has produced 65 Nobel Prize winners, 15 Fields Medals, 21 National Medals of Science, 11 National Humanities Medals, 3 US Presidents and 12 US Supreme Court Justices.

Platform

Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California. 

Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.

This content is rated 4.5 out of 5
(no review)
This content is rated 4.5 out of 5
(no review)
Complete this resource to write a review