Algorithms, Part I Course (Princeton) |
BrowseComputer ScienceAlgorithmsInstructors: Kevin Wayne InstructorsWe asked all learners to give feedback on our instructors based on the quality of their teaching style. 4. 8 (1, 818 ratings)Princeton University5 Courses1, 808, 222 learnersPrinceton University7 Courses1, 853, 733 learners1, 344, 087 already enrolledGain insight into a topic and learn the fundamentals. Intermediate levelSome related experience requiredFlexible scheduleApprox. 54 hoursLearn at your own paceMost learners liked this courseGain insight into a topic and learn the fundamentals. 54 hoursLearn at your own paceMost learners liked this courseAboutModulesRecommendationsTestimonialsReviewsDetails to knowAssessments10 assignmentsThere are 13 modules in this courseWelcome to Algorithms, Part I. What’s included1 video2 readings1 programming assignmentWe 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. What’s included5 videos2 readings1 assignment1 programming assignmentThe 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. What’s included6 videos1 reading1 assignmentWe 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. What’s included6 videos2 readings1 assignment1 programming assignmentWe 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. What’s included6 videos1 reading1 assignmentWe 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. What’s included5 videos2 readings1 assignment1 programming assignmentWe 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. What’s included4 videos1 reading1 assignmentWe 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. What’s included4 videos2 readings1 assignment1 programming assignmentWe 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. What’s included6 videos1 reading1 assignmentIn 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 search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce B-trees, a generalization of 2−3 trees that are widely used to implement file systems. What’s included3 videos2 readings1 assignmentWe 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 consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles. What’s included5 videos1 reading1 programming assignmentWe 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 strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constant-time performance for search and insert under the uniform hashing assumption. What’s included4 videos2 readings1 assignmentWe consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors. What’s included4 videos1 readingInstructorsInstructor ratingsInstructor ratingsWe asked all learners to give feedback on our instructors based on the quality of their teaching style. 8 (1, 818 ratings)Princeton University5 Courses1, 808, 222 learnersPrinceton University7 Courses1, 853, 733 learnersOffered byOffered byPrinceton UniversityPrinceton 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. Recommended if you’re interested in AlgorithmsWhy people choose for their careerFelipe M. Learner since 2018″To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood. “Jennifer J. Learner since 2020″I directly applied the concepts and skills I learned from my courses to an exciting new project at work. “Larry W. Learner since 2021″When I need courses on topics that my university doesn’t offer, is one of the best places to go. “”Learning isn’t just about being better at your job: it’s so much more than that. allows me to learn without limits. “Learner reviewsShowing 3 of 114304. 911, 430 reviews5 stars89. 37%4 stars8. 72%3 stars1. 08%2 stars0. 25%1 star0. 56%5Reviewed on May 10, 2019New to Algorithms? Start here. Open new doors with PlusUnlimited access to 7, 000+ world-class courses, hands-on projects, and job-ready certificate programs – all included in your subscriptionAdvance your career with an online degreeEarn a degree from world-class universities – 100% onlineJoin over 3, 400 global companies that choose for BusinessUpskill your employees to excel in the digital economyFrequently asked questionsOnce you enroll, you’ll have access to all videos and programming assignments. No. All features of this course are available for free. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with this course.