Explore
For Enterprise
Join for Free
Log In

artes y humanidades
Negocios
Ciencias de la Computación
Ciencia de Datos
Tecnología de información
salud
Matemáticas y Lógica
Desarrollo Personal
Ciencias Físicas e Ingeniería
Ciencias sociales
Aprendizaje de un idioma
Grados
Certificados

Explorar todo lo que Coursera tiene para ofrecer

Explorar
Buscar
For Enterprise
Inicia Sesión
Únete de forma gratuita

Course Introduction

Loading...

Algorithms, Part II

Universidad de Princeton

5 (840 calificaciones) | 98K estudiantes inscritos

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. All the features of this course are available for free. It does not offer a certificate upon completion.

Destrezas que aprenderás

Graphs, Data Structure, Algorithms, Data Compression

Revisiones

5 (840 calificaciones)

5 stars
812 ratings
4 stars
23 ratings
3 stars
2 ratings
2 stars
2 ratings
1 star
1 ratings

RK

Sep 26, 2018

An incredible course that covers a lot of vital algorithm on graphs and strings. I learned a lot of new material that I hadn't known before. Thank you very much for this amazing course!

MK

Mar 25, 2018

Excellent course that provides a good introduction to more advanced algorithms that build on those presented in part 1 of the course. Thank you Professor Sedgewick and Kevin Wayne.

De la lección

Introduction

Welcome to Algorithms, Part II.

Course Introduction9:22

Impartido por:

Robert Sedgewick
William O. Baker *39 Professor of Computer Science
Kevin Wayne
Phillip Y. Goldman '86 Senior Lecturer

Transcripción

Welcome.

I'm Bob Sedgwick, professor of computer science at Princeton.

This is our online course, Algorithms, developed by myself and

Kevin Wayne here at Princeton.

We're going to start with an overview,

a discussion of why you might want to study algorithms, and then a little bit of

discussion about the resources that you need to take this course.

So what is this course?

It's an intermediate level survey course on algorithms.

We're going to concentrate on programming and

problem solving in the context of real applications.

And our focus is going to be on two things, algorithms which are methods for

solving problems, and data structures which store the information

associated with the problem and go hands and hands with algorithms.

[COUGH] These are the basic topics that we'll cover in part one and

part two of the course.

The first part is data types sorting and searching.

We'll consider a number of data structures and algorithms that are basic

to all the methods we consider including stacks, queues, bags, and priority queues.

Then, we'll consider classic algorithms for sorting, putting things in order.

That's quick sort, merge sort, heat sort, and rated sorts.

And we'll consider classic methods for searching, including binary search trees,

red-black binary search trees, and hash tables.

The second part of the course is for more advanced algorithms,

including graph algorithms, classic graph searching algorithms,

minimum spanning tree, and algorithms.

Algorithms for processing strings including regular expressions and

data compression.

And then, some advanced algorithms that

make use of the basic algorithms that we developed earlier in the course.

So why should one study algorithms?

Well, their impact is very broad and far reaching.

From the Internet to biology, to commercial computing, to computer

graphics, security, multimedia, cellphone networks and scientific applications.

Algorithms are all around us.

They're use for movies and video games, for particle collision simulation.

They're used to study the genome and all manner of other applications.

So that's one important reason to study algorithms, there impact is broad and

far reach.

Algorithms are also interesting to study because they have ancient roots.

Now, the first algorithm we study goes back to 300 BC dating at least to Euclid.

The concept of an algorithm was formalized,

actually here at Princeton by Church and Turing in the 1930s, but

most algorithms that we consider were discovered in recent decades.

In fact, some were discovered by undergraduates in a course like this.

In these plenty of other algorithms waiting to be discovered by students

like you.

The main reason that people study algorithms is to be able to solve problems

that could not otherwise be address.

For example,

on the first lecture, we're going to talk about the network connectivity problem.

Where the problem is given a large set of items that are connected

together pair wise.

Is there a way to get from one to another with a path through the connection.

As you can see from this example, it's not clear whether or not there's such a path.

We need the computer program to do it.

In fact, we need an efficient algorithm to do it.

In this case, the answer is that there is such a path.

Another reason to study algorithms is for intellectual stimulation.

Algorithms are very interesting object to study.

Don Knuth, who wrote several books on algorithms and was a pioneer in the field,

said that, an algorithm must be seen to be believed.

You can't just think about an algorithm, you have to work with it.

Another quote from Francis Sullivan says,

the great algorithms are the poetry of computation.

Just like verse, they can be terse, allusive, dense and even mysterious.

But once unlocked they cast a brilliant new light on some aspect of computing.

Algorithms are interesting for intellectual stimulation.

Another reason many people study algorithms and

I suspect many of you is it's necessary to understand good algorithms,

efficient algorithms and data structures in order to be an efficient programmer.

Linus Torvalds who created Linux says that the difference between a bad

programmer and a good one is whether he considers his code or

his data structures more important.

Bad programmers worry about the code.

Good programmers worry about data structures and their relationships.

And I might add the algorithms to process them.

Niklaus Wirth, another pioneer in computer science,

wrote a famous book called Algorithms + Data Structures = Programs.

[COUGH] Another reason, nowadays, to study algorithms,

is that they have become a common language for understanding nature,

algorithms are computational models and algorithmic models

are replacing mathematical models and scientific inquiry.

In the 20th century, scientist develop mathematical models to

try to understand natural phenomenon, it soon

became clear that those mathematical models were difficult to solve.

It was difficult to create solutions

to be able to test hypotheses against natural phenomena.

So more and more and more, nowadays,

people are developing computational models, where they attempt to

simulate what might be happening in nature in order to try to better understand it.

Algorithms play extremely important role this process and

you'll see some of these in this course.

Another important reason is that,

if you known how to effectively use algorithms in data structures,

you're going to have a much better chance at interviewing for

a job in the technology industry than if you don't.

Here's a bunch of reasons that I just went through for studying algorithms.

Their impact's broad and far reaching.

They have old roots and present new opportunities.

That allows to solve problems that could not otherwise be addressed.

You can use them for

intellectual stimulation to become a proficient programmer.

They might unlock the secrets of life in the universe and they're good for

fun and profit.

In fact, a programmer might ask, why study anything else?

Well, there's plenty of good reasons to study other things, but

I'll submit there's no good reason not to study algorithms.

[COUGH] So for this course we have two resources that I want to talk about and

make sure that people are familiar with before entering into the content.

This is a publishing model that Kevin, Wayne, and I developed and

have been using for many years.

And we think it's a very effective way to support the kinds of

lectures that we're going to be giving in this course.

Down at the bottom ant it's optional for this course, we have a textbook, it's

a traditional textbook that extensively covered the topic in the course.

In fact, many more topics then we can present in lecture.

And then, supporting that textbook is free,

online material that we call the book site.

You can go to the book site to see the lecture slides.

But more important, there's code, there's exercises,

there's a great deal of information there.

In fact, maybe ten times, what's in the book, including a summary of the content.

So during this course you'll be referring to the book site

frequently while working online.

People often ask about prerequisites.

We're assuming that people who take this course know how to program.

You know the basics of loops, arrays, functions.

They have some exposure to object oriented programming and recursion.

We use the Java language, but we don't dwell on details of Java.

We mostly use it as an expository language.

We do some Math but not advanced Math.

If you want to review the material that we think is prerequisite to the material

in this course, you can do a quick review by looking at sections 1.1 and

1.2 of the book, either at the book site or in the textbook.

If you want an in depth review we have a full textbook

called an introduction to programming in Java, an interdisciplinary approach.

Others of booksite and a textbook as well.

But the bottom line is you should be able to program and

the quick exercise to get ready use to write a Java program on your computer

perhaps using our programming model as describe on the bookside.

We'll provide much more detailed information on that

as we get into assignments.

You can use your own programming environment if you're comfortable with

one, or you can download ours.

We have instructions on the web about how to do that.

Explora nuestro catálogo

Inscríbete de manera gratuita y obtén recomendaciones personalizadas, actualizaciones y ofertas.

Coursera brinda acceso universal a la mejor educación del mundo, al asociarse con las mejores universidades y organizaciones, para ofrecer cursos en línea.

© 2019 Coursera Inc. Todos los derechos reservados.

Descargar en la App Store

Consíguelo en Google Play

Coursera
Acerca de
Liderazgo
Empleo
Catálogo
Certificados
Certificados MasterTrack™
Grados
For Enterprise
For Government

Comunidad
Learners
Partners
Developers
Beta Testers
Translators

Conectar
Blog
Facebook
LinkedIn
Twitter
Instagram
Youtube
Blog de Tecnología

Más
Términos
Privacidad
Ayuda
Accesibilidad
Prensa
Contacto
Directorio
Afiliados