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Universidade de Princeton

Informações sobre o curso

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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.

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 6 weeks of study, 6–10 hours per week....

Legendas: Inglês, Coreano

GraphsData StructureAlgorithmsData Compression

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 6 weeks of study, 6–10 hours per week....

Legendas: Inglês, Coreano

Semana

1Welcome to Algorithms, Part II....

1 vídeo (total de (Total 9 mín.) min), 2 leituras

Welcome to Algorithms, Part II1min

Lecture Slides

We define an undirected graph API and consider the adjacency-matrix and adjacency-lists representations. We introduce two classic algorithms for searching a graph—depth-first search and breadth-first search. We also consider the problem of computing connected components and conclude with related problems and applications....

6 vídeos (total de (Total 98 mín.) min), 2 leituras, 1 teste

Graph API14min

Depth-First Search26min

Breadth-First Search13min

Connected Components18min

Graph Challenges14min

Overview1min

Lecture Slides

Interview Questions: Undirected Graphs (ungraded)6min

In this lecture we study directed graphs. We begin with depth-first search and breadth-first search in digraphs and describe applications ranging from garbage collection to web crawling. Next, we introduce a depth-first search based algorithm for computing the topological order of an acyclic digraph. Finally, we implement the Kosaraju−Sharir algorithm for computing the strong components of a digraph....

5 vídeos (total de (Total 68 mín.) min), 1 leitura, 2 testes

Digraph API4min

Digraph Search20min

Topological Sort 12min

Strong Components20min

Lecture Slides

Interview Questions: Directed Graphs (ungraded)6min

Semana

2In this lecture we study the minimum spanning tree problem. We begin by considering a generic greedy algorithm for the problem. Next, we consider and implement two classic algorithm for the problem—Kruskal's algorithm and Prim's algorithm. We conclude with some applications and open problems....

6 vídeos (total de (Total 85 mín.) min), 2 leituras, 1 teste

Greedy Algorithm12min

Edge-Weighted Graph API11min

Kruskal's Algorithm12min

Prim's Algorithm33min

MST Context10min

Overview1min

Lecture Slides

Interview Questions: Minimum Spanning Trees (ungraded)6min

In this lecture we study shortest-paths problems. We begin by analyzing some basic properties of shortest paths and a generic algorithm for the problem. We introduce and analyze Dijkstra's algorithm for shortest-paths problems with nonnegative weights. Next, we consider an even faster algorithm for DAGs, which works even if the weights are negative. We conclude with the Bellman−Ford−Moore algorithm for edge-weighted digraphs with no negative cycles. We also consider applications ranging from content-aware fill to arbitrage....

5 vídeos (total de (Total 85 mín.) min), 1 leitura, 2 testes

Shortest Paths APIs10min

Shortest Path Properties14min

Dijkstra's Algorithm18min

Edge-Weighted DAGs19min

Negative Weights21min

Lecture Slides

Interview Questions: Shortest Paths (ungraded)6min

Semana

3In this lecture we introduce the maximum flow and minimum cut problems. We begin with the Ford−Fulkerson algorithm. To analyze its correctness, we establish the maxflow−mincut theorem. Next, we consider an efficient implementation of the Ford−Fulkerson algorithm, using the shortest augmenting path rule. Finally, we consider applications, including bipartite matching and baseball elimination....

6 vídeos (total de (Total 72 mín.) min), 2 leituras, 2 testes

Ford–Fulkerson Algorithm6min

Maxflow–Mincut Theorem9min

Running Time Analysis8min

Java Implementation14min

Maxflow Applications22min

Overview

Lecture Slides

Interview Questions: Maximum Flow (ungraded)6min

In this lecture we consider specialized sorting algorithms for strings and related objects. We begin with a subroutine to sort integers in a small range. We then consider two classic radix sorting algorithms—LSD and MSD radix sorts. Next, we consider an especially efficient variant, which is a hybrid of MSD radix sort and quicksort known as 3-way radix quicksort. We conclude with suffix sorting and related applications....

6 vídeos (total de (Total 85 mín.) min), 1 leitura, 1 teste

Strings in Java17min

Key-Indexed Counting12min

LSD Radix Sort15min

MSD Radix Sort13min

3-way Radix Quicksort7min

Suffix Arrays19min

Lecture Slides

Interview Questions: Radix Sorts (ungraded)6min

Semana

4In this lecture we consider specialized algorithms for symbol tables with string keys. Our goal is a data structure that is as fast as hashing and even more flexible than binary search trees. We begin with multiway tries; next we consider ternary search tries. Finally, we consider character-based operations, including prefix match and longest prefix, and related applications....

3 vídeos (total de (Total 75 mín.) min), 2 leituras, 1 teste

Overview10min

Lecture Slides

Interview Questions: Tries (ungraded)6min

In this lecture we consider algorithms for searching for a substring in a piece of text. We begin with a brute-force algorithm, whose running time is quadratic in the worst case. Next, we consider the ingenious Knuth−Morris−Pratt algorithm whose running time is guaranteed to be linear in the worst case. Then, we introduce the Boyer−Moore algorithm, whose running time is sublinear on typical inputs. Finally, we consider the Rabin−Karp fingerprint algorithm, which uses hashing in a clever way to solve the substring search and related problems....

5 vídeos (total de (Total 75 mín.) min), 1 leitura, 2 testes

Brute-Force Substring Search10min

Knuth–Morris–Pratt33min

Boyer–Moore8min

Rabin–Karp16min

Lecture Slides10min

Interview Questions: Substring Search (ungraded)6min

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por IO•Jan 21st 2018

Pretty challenging course, but very good. Having a book is a must (at least it was for me), video lectures complement book nicely, and some topics are explained better in the Algorithms, 4th ed. book.

por AK•Apr 17th 2019

Amazing course! Loved the theory and exercises! Just a note for others: Its part 1 had almost no dependency on book, but this part 2 has some dependency (e.g. chapter on Graph) on book as well.

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....

Quando terei acesso às palestras e às tarefas?

Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

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 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.

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.

What kinds of assessments are available in this course?

Weekly programming assignments and interview questions.

The programming assignments involve either implementing algorithms and data structures (graph algorithms, tries, and the Burrows–Wheeler transform) or applying algorithms and data structures to an interesting domain (computer graphics, computational linguistics, and data compression). 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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