In this course you will learn several fundamental principles of advanced algorithm design: greedy algorithms and applications; dynamic programming and applications; NP-completeness and what it means for the algorithm designer; the design and analysis of heuristics; and more.

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!**Will I get a statement of accomplishment after completing this class?**Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.**What is the format of the class?**The class consists of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures. There will be approximately two hours worth of video content per week.**What should I know to take this class?**How to program in at least one programming language (like C, Java, or Python); familiarity with proofs, including proofs by induction and by contradiction; and some discrete probability, like how to compute the probability that a poker hand is a full house. At Stanford, a version of this course is taken by sophomore, junior, and senior-level computer science majors. While Part 2 is designed for students who have already taken Part 1, some students have successfully completed Part 2 without taking Part 1.

Dates:

- 16 March 2015, 6 weeks
- 30 June 2014, 6 weeks
- 2 September 2013, 6 weeks
- 3 December 2012, 8 weeks

Included in selections:

Algorithms

Algorithms and data structures from the beginning to advanced analysis.

Algorithms and data structures from the beginning to advanced analysis.

Алгоритмизация вычислений

1 курс МИЭМ ВШЭ, 4 кредита

1 курс МИЭМ ВШЭ, 4 кредита

More on this topic:

Discrete Optimization

Tired of solving Sudokus by hand? This class teaches you how to solve complex...

Tired of solving Sudokus by hand? This class teaches you how to solve complex...

Computing Foundations for Computational Science

Computation has long been an important tool for scientists, but the...

Computation has long been an important tool for scientists, but the...

Intro to Algorithms. Social Network Analysis

This class will give you an introduction to the design and analysis of algorithms...

This class will give you an introduction to the design and analysis of algorithms...

Introduction to Algorithms (SMA 5503)

This course teaches techniques for the design and analysis of efficient algorithms...

This course teaches techniques for the design and analysis of efficient algorithms...

Computer Algorithms in Systems Engineering

This course covers concepts of computation used in analysis of engineering systems...

This course covers concepts of computation used in analysis of engineering systems...

More from 'Computer Science':

Capstone Exam in Statistics and Data Science

Solidify and demonstrate your knowledge and abilities in probability, data analysis...

Solidify and demonstrate your knowledge and abilities in probability, data analysis...

Network Science

The course is an interdisciplinary course, focused on the emerging science of...

The course is an interdisciplinary course, focused on the emerging science of...

Gameplay Programming for Video Game Designers

Learn how to create the best gameplay by understanding algorithmic thinking...

Learn how to create the best gameplay by understanding algorithmic thinking...

Introducing Robotics: Making Robots Move

The world needs people who understand how to get robots moving.

The world needs people who understand how to get robots moving.

Python in High Performance Computing

Learn how to analyse Python programmes and identify performance barriers to...

Learn how to analyse Python programmes and identify performance barriers to...

More from 'Coursera':

First Year Teaching (Secondary Grades) - Success from the Start

Success with your students starts on Day 1. Learn from NTC's 25 years developing...

Success with your students starts on Day 1. Learn from NTC's 25 years developing...

Understanding 9/11: Why Did al Qai’da Attack America?

This course will explore the forces that led to the 9/11 attacks and the policies...

This course will explore the forces that led to the 9/11 attacks and the policies...

Aboriginal Worldviews and Education

This course will explore indigenous ways of knowing and how this knowledge can...

This course will explore indigenous ways of knowing and how this knowledge can...

Analytic Combinatorics

Analytic Combinatorics teaches a calculus that enables precise quantitative...

Analytic Combinatorics teaches a calculus that enables precise quantitative...

Accountable Talk®: Conversation that Works

Designed for teachers and learners in every setting - in school and out, in...

Designed for teachers and learners in every setting - in school and out, in...

© 2013-2019