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Algorithms, Part I
Princeton University via Coursera

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Overview
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Syllabus
Welcome to Algorithms, Part I.
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.
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.
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 singlylinked 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.
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.
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, bottomup version. We prove that any comparebased 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.
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 3way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys.
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.
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.
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 search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce Btrees, a generalization of 2−3 trees that are widely used to implement file systems.
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 kdtrees, 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.
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 strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constanttime performance for search and insert under the uniform hashing assumption.
Symbol Table Applications
We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.
Taught by
Robert Sedgewick and Kevin Wayne
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Reviews
4.5 rating, based on 59 reviews

Miguel is taking this course right now, spending 10 hours a week on it and found the course difficulty to be very hard.
Worst course I've ever taken. I was really engaged to this course and spent many hours studying, taking neat notes, researching, making diagrams and trying to understand what Sedgewick says. I have a background in programming and strong knowledge of relatively... 
Wickwack completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
This class (and part 2) are the best courses I've ever done online. The lectures are clear, concise, and interesting. The assignments are fascinating, touching on a whole range of topics (computational geometry, physics, etc.) while allowing us to use... 
Wei completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
Professor Sedgewick's explanation of algorithms and his use of visuals were excellent and instrumental in helping me to understand the content.
The exercises tend to have a few challenging questions but a couple of questions which force you to simulate a computer and run the algorithms. Personally, I dislike these type of questions. On the other hand, the programming assignments are fun and force students to think out of the box. Also, the grading system is very detailed and gives a lot of useful feedback.
In general, this course is an great fit for anyone who wishes to learn about algorithms and is new to the field. 
Ilya completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
This is kind of specific course on algorithms  authors have their own Java library, specific interests in applications and even their own terminology sometimes. This is course about Java realization of algorithms, not about math.
The best part of the course is of course problem sets with rigorous tests. There are a lot of additional exercises in their book if you're interested in programming of algorithms  many of them are from job interviews. 
Mark completed this course.
This course is an algorithms class intended to be the 2nd course taken by CS students at Princeton. From what I could tell the course was pretty true to the actual Princeton class, and the automated grader was great. This algorithms class was well designed and I’ll probably take the followup class. 
Anonymous completed this course.
this algorithm course is a practical one. this course uses java as the main algorithm description tool, and students will have much insight of java. it's useful to learn the different performance of different implementations of an algorithm. there's... 
Anonymous completed this course.
Difficult class, not for beginners. PowerPoint presentation slides are boring. It seems Professor Sedgewick has taught this class so many times that he've got tired of it. 
Anonymous completed this course.
This is probably one of the best class I took in Algorithm. Yes, the assignments are challenging but you learn quite a bit by just doing so. Furthermore, I found the lectures well done. I find the instructor quite interesting and am motivated to do the next course given by this instructor. 
Anonymous completed this course.
I found this course quite challenging, but learned a lot. Discussion forums were very helpful, much higher standard than other courses I have taken. I enjoyed the lectures. Looking forward to part II. Lack of Java knowledge does make the course very difficult. 
Ken completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
Because I don't know Java (yet) and the homework can only be submitted in Java, I audited this course. Time well spent! Even without working the exercises, the lectures were easy to follow and highly interesting. I picked up several things that will likely help me write better code. 
Anonymous completed this course.
Great course. Lectures are very well done, best I have seen so far. Programming assignments were also quite good even though they are in Java, which I didn't know at the start of the course. Problem sets were good, but some work could be improve the interface. Everything was on time. 
Anonymous completed this course.
I read some books and do some classes about algorithms,but I think this class is best way to learn algorithms.Prof Sedgewick explain algorithms and data structures very good. 
Anonymous completed this course.
I had some programming experience  mostly in Python  when started this course and it was very useful, interesting and inspiring. Prof. Sedgewick is a very good teacher. 
Sergey completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
This is the best course in Algorithms I've found on the internet. I've done it twice actually.
The lectures are clear and concise, the simulations explain clearly the algorithms in study.
The homeworks are challenging and interesting. Each assignment took me about 5 hours on average. It is a good refresh on Java as well. Looking forward for the next part of the course.
Sergey.

赵志勇 completed this course, spending 10 hours a week on it and found the course difficulty to be hard.
This is the most helpful algorithms course that I have taken. It's easy to understand each algorithm with the illustrations. The professor's tone is slow so I can catch it. Anyway, I will recommend it to my classmates and friends whenever talking about algorithm courses. 
Tony_chau completed this course, spending 7 hours a week on it and found the course difficulty to be hard.
The great course lectures are doing well and the best I have seen. Programming homework is also good, even if they are Java, I do not know at the beginning of the course. The problem set is good, but some work can improve the interface. Everything is on time 
Thol completed this course.

Anonymous is taking this course right now.

Zuzana is taking this course right now.

Youcyuan completed this course, spending 10 hours a week on it and found the course difficulty to be hard.