Algorithms, Part I

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  • 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.




Kevin Wayne
Senior Lecturer
Computer Science

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


普林斯顿大学(Princeton University),又称普林斯顿大学,是一所美国私立大学,位于美国新泽西州普林斯顿镇。它成立于 1746 年,是美国第四古老的高等教育机构。


该校曾培养出 65 位诺贝尔奖得主、15 位菲尔兹奖得主、21 位国家科学奖得主、11 位国家人文奖得主、3 位美国总统和 12 位美国最高法院大法官。


Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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