[Coursera] Algorithms Part I
Kevin Wayne and Robert Sedgewick (Princeton University)

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algo-p1 (89 files)
lectures/week0/Algorithms Part I 0.0 Course Introduction (922) ✐ Quiz Attempted.mp444.23MB
lectures/week1/01-union-find/Algorithms Part I 1.0 Dynamic Connectivity (1022) ✐ Quiz Attempted.mp416.80MB
lectures/week1/01-union-find/Algorithms Part I 1.1 Quick Find (1018) ✐ Quiz Attempted.mp420.37MB
lectures/week1/01-union-find/Algorithms Part I 1.2 Quick Union (750) ✐ Quiz Attempted.mp411.50MB
lectures/week1/01-union-find/Algorithms Part I 1.3 Quick-Union Improvements (1302) ✐ Quiz Attempted.mp463.20MB
lectures/week1/01-union-find/Algorithms Part I 1.4 Union-Find Applications (922).mp420.79MB
lectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.0 Analysis of Algorithms Introduction (814) ✐ Quiz Attempted.mp414.01MB
lectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.1 Observations (1005) ✐ Quiz Attempted.mp415.07MB
lectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.2 Mathematical Models (1248) ✐ Quiz Attempted.mp420.67MB
lectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.3 Order-of-Growth Classifications (1439) ✐ Quiz Attempted.mp420.34MB
lectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.4 Theory of Algorithms (1135).mp419.93MB
lectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.5 Memory (811).mp412.94MB
lectures/week2/01-stacks-and-queues/Algorithms Part I 3.0 Stacks (1624).mp426.50MB
lectures/week2/01-stacks-and-queues/Algorithms Part I 3.1 Resizing Arrays (956).mp417.07MB
lectures/week2/01-stacks-and-queues/Algorithms Part I 3.2 Queues (433).mp48.15MB
lectures/week2/01-stacks-and-queues/Algorithms Part I 3.3 Generics (926).mp416.10MB
lectures/week2/01-stacks-and-queues/Algorithms Part I 3.4 Iterators (716).mp411.60MB
lectures/week2/01-stacks-and-queues/Algorithms Part I 3.5 Stack and Queue Applications (1325) (optional).mp458.18MB
lectures/week2/02-elementary-sorts/Algorithms Part I 4.0 Sorting Introduction (1443).mp442.61MB
lectures/week2/02-elementary-sorts/Algorithms Part I 4.1 Selection Sort (659).mp410.73MB
lectures/week2/02-elementary-sorts/Algorithms Part I 4.2 Insertion Sort (928).mp414.77MB
lectures/week2/02-elementary-sorts/Algorithms Part I 4.3 Shellsort (1048).mp426.78MB
lectures/week2/02-elementary-sorts/Algorithms Part I 4.4 Shuffling (739).mp412.19MB
lectures/week2/02-elementary-sorts/Algorithms Part I 4.5 Convex Hull (1350).mp446.12MB
lectures/week3/01-mergesort/Algorithms Part I 5.0 Mergesort (2354).mp470.76MB
lectures/week3/01-mergesort/Algorithms Part I 5.1 Bottom-up Mergesort (320).mp45.63MB
lectures/week3/01-mergesort/Algorithms Part I 5.2 Sorting Complexity (905).mp414.45MB
lectures/week3/01-mergesort/Algorithms Part I 5.3 Comparators (643).mp419.68MB
lectures/week3/01-mergesort/Algorithms Part I 5.4 Stability (539).mp49.17MB
lectures/week3/02-quicksort/Algorithms Part I 6.0 Quicksort (1933).mp429.97MB
lectures/week3/02-quicksort/Algorithms Part I 6.1 Selection (708).mp428.88MB
lectures/week3/02-quicksort/Algorithms Part I 6.2 Duplicate Keys (1125).mp418.90MB
lectures/week3/02-quicksort/Algorithms Part I 6.3 System Sorts (1150).mp419.95MB
lectures/week4/01-priority-queues/Algorithms Part I 7.0 APIs and Elementary Implementations (1252).mp420.90MB
lectures/week4/01-priority-queues/Algorithms Part I 7.1 Binary Heaps (2336).mp435.07MB
lectures/week4/01-priority-queues/Algorithms Part I 7.2 Heapsort (1429).mp421.49MB
lectures/week4/01-priority-queues/Algorithms Part I 7.3 Event-Driven Simulation (2238) (optional).mp440.44MB
lectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.0 Symbol Table API (2130).mp434.18MB
lectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.1 Elementary Implementations (903).mp413.56MB
lectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.2 Ordered Operations (626).mp418.08MB
lectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.3 Binary Search Trees (1956).mp454.54MB
lectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.4 Ordered Operations in BSTs (1031).mp425.49MB
lectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.5 Deletion in BSTs (952).mp434.84MB
lectures/week5/01-balanced-search-tree/Algorithms Part I 9.0 2-3 Search Trees (1655).mp426.26MB
lectures/week5/01-balanced-search-tree/Algorithms Part I 9.1 Red-Black BSTs (3530).mp455.25MB
lectures/week5/01-balanced-search-tree/Algorithms Part I 9.2 B-Trees (1036) (optional).mp444.97MB
lectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.0 1d Range Search (851).mp412.75MB
lectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.1 Line Segment Intersection (546).mp418.91MB
lectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.2 Kd-Trees (2907).mp4131.20MB
lectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.3 Interval Search Trees (1347).mp447.58MB
lectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.4 Rectangle Intersection (810).mp428.76MB
lectures/week6/01-hashtables/Algorithms Part I 11.0 Hash Functions (1813).mp452.10MB
lectures/week6/01-hashtables/Algorithms Part I 11.1 Separate Chaining (728).mp410.84MB
lectures/week6/01-hashtables/Algorithms Part I 11.2 Linear Probing (1437).mp421.25MB
lectures/week6/01-hashtables/Algorithms Part I 11.3 Hash Table Context (1009).mp415.07MB
lectures/week6/01-hashtables/Algorithms Part I 11.4 Symbol Table Applications Sets (504) (optional).mp417.11MB
lectures/week6/01-hashtables/Algorithms Part I 11.5 Symbol Table Applications Dictionary Clients (540) (optional).mp416.04MB
lectures/week6/01-hashtables/Algorithms Part I 11.6 Symbol Table Applications Indexing Clients (857) (optional).mp425.77MB
lectures/week6/01-hashtables/Algorithms Part I 11.7 Symbol Table Applications Sparse Vectors (741) (optional).mp411.78MB
Type: Course

title= {[Coursera] Algorithms Part I},
keywords= {},
journal= {},
author= {Kevin Wayne and Robert Sedgewick (Princeton University)},
year= {},
url= {},
license= {},
abstract= {About this course: 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.

## 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 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.

## 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, 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.

## 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 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) 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 B-trees, 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 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.

## 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 constant-time 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. 
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