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Universidad de Princeton

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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.

Comienza de inmediato y aprende a tu propio ritmo.

Restablece las fechas lĆmite en funciĆ³n de tus horarios.

Sugerido: 6 weeks of study, 6ā10 hours per week....

SubtĆtulos: InglĆ©s (English), Coreano

Data StructurePriority QueueAlgorithmsJava Programming

Comienza de inmediato y aprende a tu propio ritmo.

Restablece las fechas lĆmite en funciĆ³n de tus horarios.

Sugerido: 6 weeks of study, 6ā10 hours per week....

SubtĆtulos: InglĆ©s (English), Coreano

Semana

1Welcome to Algorithms, Part I....

1 video (Total 9 minutos), 2 readings

Welcome to Algorithms, Part I1m

Lecture Slides

We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the unionāfind data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the unionāfind data type to the percolation problem from physical chemistry....

5 videos (Total 51 minutos), 2 readings, 2 quizzes

Quick Find10m

Quick Union7m

Quick-Union Improvements13m

UnionāFind Applications9m

Overview1m

Lecture Slides

Interview Questions: UnionāFind (ungraded)

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 (Total 66 minutos), 1 reading, 1 quiz

Observations10m

Mathematical Models12m

Order-of-Growth Classifications14m

Theory of Algorithms11m

Memory8m

Lecture Slides

Interview Questions: Analysis of Algorithms (ungraded)

Semana

2We 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 featuresāgenerics and iteratorsāthat simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems....

6 videos (Total 61 minutos), 2 readings, 2 quizzes

Stacks16m

Resizing Arrays9m

Queues4m

Generics9m

Iterators7m

Stack and Queue Applications (optional)13m

Overview1m

Lecture Slides

Interview Questions: Stacks and Queues (ungraded)

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 (Total 63 minutos), 1 reading, 1 quiz

Selection Sort6m

Insertion Sort9m

Shellsort10m

Shuffling7m

Convex Hull13m

Lecture Slides

Interview Questions: Elementary Sorts (ungraded)

Semana

3We 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 (Total 49 minutos), 2 readings, 2 quizzes

Overview

Lecture Slides

Interview Questions: Mergesort (ungraded)

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 (Total 50 minutos), 1 reading, 1 quiz

Lecture Slides

Interview Questions: Quicksort (ungraded)

Semana

4We 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 (Total 74 minutos), 2 readings, 2 quizzes

Binary Heaps23m

Heapsort14m

Event-Driven Simulation (optional)22m

Overview10m

Lecture Slides

Interview Questions: Priority Queues (ungraded)

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 (Total 77 minutos), 1 reading, 1 quiz

Elementary Implementations9m

Ordered Operations6m

Binary Search Trees19m

Ordered Operations in BSTs10m

Deletion in BSTs9m

Lecture Slides

Interview Questions: Elementary Symbol Tables (ungraded)8m

comenzĆ³ una nueva carrera despuĆ©s de completar estos cursos

consiguiĆ³ un beneficio tangible en su carrera profesional gracias a este curso

consiguiĆ³ un aumento de sueldo o ascenso

por RMā¢Jun 1st 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 RPā¢Jun 11th 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!

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....

ĀæCuĆ”ndo podrĆ© acceder a las lecciones y tareas?

Una vez que te inscribes para obtener un Certificado, tendrĆ”s acceso a todos los videos, cuestionarios y tareas de programaciĆ³n (si corresponde). Las tareas calificadas por compaĆ±eros solo pueden enviarse y revisarse una vez que haya comenzado tu sesiĆ³n. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

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, redāblack 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, KosarajuāSharir, Kruskal, Prim, Dijkistra, BellmanāFord, FordāFulkerson, LSD radix sort, MSD radix sort, 3-way radix quicksort, multiway tries, ternary search tries, KnuthāMorrisāPratt, BoyerāMoore, RabināKarp, regular expression matching, run-length coding, Huffman coding, LZW compression, and the BurrowsāWheeler transform.

What 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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