Coursera- Algorithm Design & Analysis 2

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Added on October 27, 2015 by stds_sakin Other > Tutorials
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Coursera- Algorithm Design & Analysis 2 (Size: 1.67 GB)

		1 - 1 - Application_ Internet Routing (11 min).mp4	13.85 MB
		1 - 2 - Application_ Sequence Alignment (9 min).mp4	11.1 MB
		10 - 1 - Introduction_ Weighted Independent Sets in Path Graphs (8 min).mp4	11.26 MB
		10 - 2 - WIS in Path Graphs_ Optimal Substructure (9 min).mp4	13.52 MB
		10 - 3 - WIS in Path Graphs_ A Linear-Time Algorithm (10 min).mp4	14.54 MB
		10 - 4 - WIS in Path Graphs_ A Reconstruction Algorithm (7 min).mp4	9.62 MB
		10 - 5 - Principles of Dynamic Programming (8 min).mp4	12.27 MB
		11 - 1 - The Knapsack Problem (10 min).mp4	14.08 MB
		11 - 2 - A Dynamic Programming Algorithm (10 min).mp4	13.61 MB
		12 - 1 - Optimal Substructure (14 min).mp4	19.54 MB
		12 - 2 - A Dynamic Programming Algorithm (12 min).mp4	16.71 MB
		13 - 1 - Problem Definition (12 min).mp4	17.19 MB
		13 - 2 - Optimal Substructure (9 min).mp4	13.94 MB
		13 - 3 - Proof of Optimal Substructure (7 min).mp4	9.86 MB
		13 - 4 - A Dynamic Programming Algorithm I (10 min).mp4	14.05 MB
		13 - 5 - A Dynamic Programming Algorithm II (9 min).mp4	11.94 MB
		14 - 1 - Single-Source Shortest Paths, Revisted (11 min).mp4	15.15 MB
		14 - 2 - Optimal Substructure (11 min).mp4	15.27 MB
		14 - 3 - The Basic Algorithm I (9 min).mp4	12.11 MB
		14 - 4 - The Basic Algorithm II (11 min).mp4	13.61 MB
		14 - 5 - Detecting Negative Cycles (9 min).mp4	12.78 MB
		14 - 6 - A Space Optimization (12 min).mp4	18.34 MB
		14 - 7 - Internet Routing I [Optional] (11 min).mp4	16.72 MB
		14 - 8 - Internet Routing II [Optional] (7 min).mp4	10.68 MB
		15 - 1 - Problem Definition (7 min).mp4	9.48 MB
		15 - 2 - Optimal Substructure (12 min).mp4	16.53 MB
		15 - 3 - The Floyd-Warshall Algorithm (13 min).mp4	18.89 MB
		15 - 4 - A Reweighting Technique (14 min).mp4	19.62 MB
		15 - 5 - Johnson_'s Algorithm I (11 min).mp4	15.49 MB
		15 - 6 - Johnson_'s Algorithm II (11 min).mp4	16.86 MB

Description

About the Course
In this course you will learn several fundamental principles of advanced algorithm design. You'll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You'll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments. You’ll learn what NP-completeness and the famous “P vs. NP” problem mean for the algorithm designer. Finally, we’ll study several strategies for dealing with hard (i.e., NP-complete problems), including the design and analysis of heuristics. Learn how shortest-path algorithms from the 1950s (i.e., pre-ARPANET!) govern the way that your Internet traffic gets routed today; why efficient algorithms are fundamental to modern genomics; and how to make a million bucks in prize money by “just” solving a math problem!
Course Syllabus
Weeks 1 and 2: The greedy algorithm design paradigm. Applications to optimal caching and scheduling. Minimum spanning trees and applications to clustering. The union-find data structure. Optimal data compression.

Weeks 3 and 4: The dynamic programming design paradigm. Applications to the knapsack problem, sequence alignment, shortest-path routing, and optimal search trees.

Weeks 5 and 6: Intractable problems and what to do about them. NP-completeness and the P vs. NP question. Solvable special cases. Heuristics with provable performance guarantees. Local search. Exponential-time algorithms that beat brute-force search.

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. The course assumes familiarity with some of the topics from Algo 1 --- especially asymptotic analysis, basic data structures, and basic graph algorithms.

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 favorite for additional information.

Instructor
Tim Roughgarden, Stanford University

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1.67 GB

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Coursera- Algorithm Design & Analysis 2