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

folder algo-p1 (89 files)
filelectures/week0/Algorithms Part I 0.0 Course Introduction (922) ✐ Quiz Attempted.mp4 44.23MB
filelectures/week1/01-union-find/Algorithms Part I 1.0 Dynamic Connectivity (1022) ✐ Quiz Attempted.mp4 16.80MB
filelectures/week1/01-union-find/Algorithms Part I 1.1 Quick Find (1018) ✐ Quiz Attempted.mp4 20.37MB
filelectures/week1/01-union-find/Algorithms Part I 1.2 Quick Union (750) ✐ Quiz Attempted.mp4 11.50MB
filelectures/week1/01-union-find/Algorithms Part I 1.3 Quick-Union Improvements (1302) ✐ Quiz Attempted.mp4 63.20MB
filelectures/week1/01-union-find/Algorithms Part I 1.4 Union-Find Applications (922).mp4 20.79MB
filelectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.0 Analysis of Algorithms Introduction (814) ✐ Quiz Attempted.mp4 14.01MB
filelectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.1 Observations (1005) ✐ Quiz Attempted.mp4 15.07MB
filelectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.2 Mathematical Models (1248) ✐ Quiz Attempted.mp4 20.67MB
filelectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.3 Order-of-Growth Classifications (1439) ✐ Quiz Attempted.mp4 20.34MB
filelectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.4 Theory of Algorithms (1135).mp4 19.93MB
filelectures/week1/02-analysis-of-algorithms/Algorithms Part I 2.5 Memory (811).mp4 12.94MB
filelectures/week2/01-stacks-and-queues/Algorithms Part I 3.0 Stacks (1624).mp4 26.50MB
filelectures/week2/01-stacks-and-queues/Algorithms Part I 3.1 Resizing Arrays (956).mp4 17.07MB
filelectures/week2/01-stacks-and-queues/Algorithms Part I 3.2 Queues (433).mp4 8.15MB
filelectures/week2/01-stacks-and-queues/Algorithms Part I 3.3 Generics (926).mp4 16.10MB
filelectures/week2/01-stacks-and-queues/Algorithms Part I 3.4 Iterators (716).mp4 11.60MB
filelectures/week2/01-stacks-and-queues/Algorithms Part I 3.5 Stack and Queue Applications (1325) (optional).mp4 58.18MB
filelectures/week2/02-elementary-sorts/Algorithms Part I 4.0 Sorting Introduction (1443).mp4 42.61MB
filelectures/week2/02-elementary-sorts/Algorithms Part I 4.1 Selection Sort (659).mp4 10.73MB
filelectures/week2/02-elementary-sorts/Algorithms Part I 4.2 Insertion Sort (928).mp4 14.77MB
filelectures/week2/02-elementary-sorts/Algorithms Part I 4.3 Shellsort (1048).mp4 26.78MB
filelectures/week2/02-elementary-sorts/Algorithms Part I 4.4 Shuffling (739).mp4 12.19MB
filelectures/week2/02-elementary-sorts/Algorithms Part I 4.5 Convex Hull (1350).mp4 46.12MB
filelectures/week3/01-mergesort/Algorithms Part I 5.0 Mergesort (2354).mp4 70.76MB
filelectures/week3/01-mergesort/Algorithms Part I 5.1 Bottom-up Mergesort (320).mp4 5.63MB
filelectures/week3/01-mergesort/Algorithms Part I 5.2 Sorting Complexity (905).mp4 14.45MB
filelectures/week3/01-mergesort/Algorithms Part I 5.3 Comparators (643).mp4 19.68MB
filelectures/week3/01-mergesort/Algorithms Part I 5.4 Stability (539).mp4 9.17MB
filelectures/week3/02-quicksort/Algorithms Part I 6.0 Quicksort (1933).mp4 29.97MB
filelectures/week3/02-quicksort/Algorithms Part I 6.1 Selection (708).mp4 28.88MB
filelectures/week3/02-quicksort/Algorithms Part I 6.2 Duplicate Keys (1125).mp4 18.90MB
filelectures/week3/02-quicksort/Algorithms Part I 6.3 System Sorts (1150).mp4 19.95MB
filelectures/week4/01-priority-queues/Algorithms Part I 7.0 APIs and Elementary Implementations (1252).mp4 20.90MB
filelectures/week4/01-priority-queues/Algorithms Part I 7.1 Binary Heaps (2336).mp4 35.07MB
filelectures/week4/01-priority-queues/Algorithms Part I 7.2 Heapsort (1429).mp4 21.49MB
filelectures/week4/01-priority-queues/Algorithms Part I 7.3 Event-Driven Simulation (2238) (optional).mp4 40.44MB
filelectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.0 Symbol Table API (2130).mp4 34.18MB
filelectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.1 Elementary Implementations (903).mp4 13.56MB
filelectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.2 Ordered Operations (626).mp4 18.08MB
filelectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.3 Binary Search Trees (1956).mp4 54.54MB
filelectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.4 Ordered Operations in BSTs (1031).mp4 25.49MB
filelectures/week4/02-elementary-symbol-tables/Algorithms Part I 8.5 Deletion in BSTs (952).mp4 34.84MB
filelectures/week5/01-balanced-search-tree/Algorithms Part I 9.0 2-3 Search Trees (1655).mp4 26.26MB
filelectures/week5/01-balanced-search-tree/Algorithms Part I 9.1 Red-Black BSTs (3530).mp4 55.25MB
filelectures/week5/01-balanced-search-tree/Algorithms Part I 9.2 B-Trees (1036) (optional).mp4 44.97MB
filelectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.0 1d Range Search (851).mp4 12.75MB
filelectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.1 Line Segment Intersection (546).mp4 18.91MB
filelectures/week5/02-geometric-applications-of-BSTs/Algorithms Part I 10.2 Kd-Trees (2907).mp4 131.20MB
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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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