Coursera- Algorithm Design & Analysis 1

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Added on October 27, 2015 by stds_sakin Other > Tutorials
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Coursera- Algorithm Design & Analysis 1 (Size: 1.4 GB)

		1 - 1 - Why Study Algorithms _ (4 min).mp4	7.98 MB
		1 - 2 - Integer Multiplication (9 min).mp4	16.86 MB
		1 - 3 - Karatsuba Multiplication (13 min).mp4	21.66 MB
		1 - 4 - About the Course (17 min).mp4	17.37 MB
		1 - 5 - Merge Sort_ Motivation and Example (9 min).mp4	10.68 MB
		1 - 6 - Merge Sort_ Pseudocode (13 min).mp4	15.01 MB
		1 - 7 - Merge Sort_ Analysis (9 min).mp4	11.31 MB
		1 - 8 - Guiding Principles for Analysis of Algorithms (15 min).mp4	17.99 MB
		10 - 1 - Graph Search - Overview (23 min).mp4	23.25 MB
		10 - 2 - Breadth-First Search (BFS)_ The Basics (14 min).mp4	14.28 MB
		10 - 3 - BFS and Shortest Paths (8 min).mp4	7.9 MB
		10 - 4 - BFS and Undirected Connectivity (13 min).mp4	13.7 MB
		10 - 5 - Depth-First Search (DFS)_ The Basics (7 min).mp4	7.16 MB
		10 - 6 - Topological Sort (22 min).mp4	22.08 MB
		10 - 7 - Computing Strong Components_ The Algorithm (29 min).mp4	29.5 MB
		10 - 8 - Computing Strong Components_ The Analysis (26 min).mp4	26.77 MB
		10 - 9 - Structure of the Web [Optional] (19 min).mp4	18.63 MB
		11 - 1 - Dijkstra_'s Shortest-Path Algorithm (21 min).mp4	20.96 MB
		11 - 2 - Dijkstra_'s Algorithm_ Examples (13 min).mp4	12.88 MB
		11 - 3 - Correctness of Dijkstra_'s Algorithm [Advanced - Optional] (19 min).mp4	20.31 MB
		11 - 4 - Dijkstra_'s Algorithm_ Implementation and Running Time (26 min).mp4	26.46 MB
		12 - 1 - Data Structures_ Overview (5 min).mp4	4.66 MB
		12 - 2 - Heaps_ Operations and Applications (18 min).mp4	18.62 MB
		12 - 3 - Heaps_ Implementation Details [Advanced - Optional] (21 min).mp4	20.92 MB
		13 - 1 - Balanced Search Trees_ Operations and Applications (11 min).mp4	10.99 MB
		13 - 2 - Binary Search Tree Basics, Part I (13 min).mp4	13.28 MB
		13 - 3 - Binary Search Tree Basics, Part II (30 min).mp4	29.97 MB
		13 - 4 - Red-Black Trees (21 min).mp4	21.83 MB
		13 - 5 - Rotations [Advanced - Optional] (8 min).mp4	10.32 MB
		13 - 6 - Insertion in a Red-Black Tree [Advanced] (15 min).mp4	20.78 MB

Description

About the Course
In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?

Course Syllabus
Week 1: Introduction. Asymptotic analysis including big-oh notation. Divide-and-conquer algorithms for sorting, counting inversions, matrix multiplication, and closest pair.

Week 2: Running time analysis of divide-and-conquer algorithms. The master method. Introduction to randomized algorithms, with a probability review. QuickSort.

Week 3: More on randomized algorithms and probability. Computing the median in linear time. A randomized algorithm for the minimum graph cut problem.

Week 4: Graph primitives. Depth- and breadth-first search. Connected components in undirected graphs. Topological sort in directed acyclic graphs. Strongly connected components in directed graphs.

Week 5: Dijkstra's shortest-path algorithm. Introduction to data structures. Heaps and applications.

Week 6: Further data structures. Hash tables and applications. Balanced binary search trees.

Recommended Background
How to program in at least one programming language (like C, Java, or Python); and familiarity with proofs, including proofs by induction and by contradiction. At Stanford, a version of this course is taken by sophomore, junior, and senior-level computer science majors.

Suggested Readings
No specific textbook is required for the course. Much of the course material is covered by the well-known textbooks on algorithms, and the student is encouraged to consult their favourite for additional information.

Instructor
Tim Roughgarden, Stanford University

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Coursera- Algorithm Design & Analysis 1