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.
oferecido por
Algoritmos, Parte I
Universidade de Princeton
Informações sobre o curso
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Resultados de carreira do aprendiz
32%
comecei uma nova carreira após concluir estes cursos
34%
consegui um benefício significativo de carreira com este curso
17%
recebi um aumento ou promoção
Habilidades que você terá
Data StructureAlgorithmsJava Programming
Resultados de carreira do aprendiz
32%
comecei uma nova carreira após concluir estes cursos
34%
consegui um benefício significativo de carreira com este curso
17%
recebi um aumento ou promoção
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 33 horas para completar
Sugerido: 6 weeks of study, 6–10 hours per week.
Inglês
Legendas: Inglês, Coreano, Russo
Programa - O que você aprenderá com este curso
10 minutos para concluir
Course Introduction
Welcome to Algorithms, Part I.
10 minutos para concluir
1 vídeo (Total 9 mín.), 2 leituras
1 vídeos
2 leituras
Welcome to Algorithms, Part I1min
Lecture Slides
9 horas para concluir
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.
9 horas para concluir
5 vídeos (Total 51 mín.), 2 leituras, 2 testes
5 videos
Dynamic Connectivity10min
Quick Find10min
Quick Union7min
Quick-Union Improvements13min
Union−Find Applications9min
2 leituras
Overview1min
Lecture Slides
1 exercício prático
Interview Questions: Union–Find (ungraded)
1 hora para concluir
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.
1 hora para concluir
6 vídeos (Total 66 mín.), 1 leitura, 1 teste
6 videos
Observations10min
Mathematical Models12min
Order-of-Growth Classifications14min
Theory of Algorithms11min
Memory8min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Analysis of Algorithms (ungraded)
9 horas para concluir
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.
9 horas para concluir
6 vídeos (Total 61 mín.), 2 leituras, 2 testes
6 videos
Stacks16min
Resizing Arrays9min
Queues4min
Generics9min
Iterators7min
Stack and Queue Applications (optional)13min
2 leituras
Overview1min
Lecture Slides
1 exercício prático
Interview Questions: Stacks and Queues (ungraded)
1 hora para concluir
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.
1 hora para concluir
6 vídeos (Total 63 mín.), 1 leitura, 1 teste
6 videos
Sorting Introduction14min
Selection Sort6min
Insertion Sort9min
Shellsort10min
Shuffling7min
Convex Hull13min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Elementary Sorts (ungraded)
9 horas para concluir
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.
9 horas para concluir
5 vídeos (Total 49 mín.), 2 leituras, 2 testes
2 leituras
Overview
Lecture Slides
1 exercício prático
Interview Questions: Mergesort (ungraded)
1 hora para concluir
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.
1 hora para concluir
4 vídeos (Total 50 mín.), 1 leitura, 1 teste
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Quicksort (ungraded)
9 horas para concluir
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.
9 horas para concluir
4 vídeos (Total 74 mín.), 2 leituras, 2 testes
4 videos
Binary Heaps23min
Heapsort14min
Event-Driven Simulation (optional)22min
2 leituras
Overview10min
Lecture Slides
1 exercício prático
Interview Questions: Priority Queues (ungraded)
1 hora para concluir
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.
1 hora para concluir
6 vídeos (Total 77 mín.), 1 leitura, 1 teste
6 videos
Symbol Table API21min
Elementary Implementations9min
Ordered Operations6min
Binary Search Trees19min
Ordered Operations in BSTs10min
Deletion in BSTs9min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Elementary Symbol Tables (ungraded)8min
Avaliações
Principais avaliações do ALGORITMOS, PARTE I
por RMMay 31, 2017
This is a great class. I learned / re-learned a ton. The assignments were challenge and left a definite feel of accomplishment. The programming environment and automated grading system were excellent.
por BJJun 2, 2018
Good contents and the logic of the whole course structure is very clear for a novice like me. The weekly homework is also awesome. Would recommend to anyone who wants to learn about computer science.
por RPJun 10, 2017
Incredible learning experience. Every programmer in industry should take this course if only to dispel the idea that with the advent of cloud computing exponential algorithms can still ruin your day!
por KRDec 1, 2016
An amazing course which enables one to appreciate and analyze various algorithms used in various applications. Must take for anyone who wants to build a career in computer science or related fields.
oferecido por
Universidade de Princeton
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Once you enroll, you’ll have access to all videos and programming assignments.
Do I need to pay for this course?
No. All features of this course are available for free.
Can I earn a certificate in this course?
No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with this course.
I have no familiarity with Java programming. Can I still take 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.
Which algorithms and data structures are covered in this course?
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.
Which kinds of assessments are available in this course?
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.
I am/was not a Computer Science major. Is this course for me?
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.
How does this course differ from Design and Analysis of Algorithms?
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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