Intro to Theoretical Computer Science

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Description

Solving Hard Problems

This class teaches you about basic concepts in theoretical computer science—such as NP-completeness—and what they imply for…

Class Summary

This class teaches you about basic concepts in theoretical computer science -- such as NP-completeness -- and what they imply for solving tough algorithmic problems.

What Should I Know?

You should have a basic understanding of algorithms (such as CS215) and programming (such as CS101). No prior knowledge about theoretical computer science required!

What Will I Learn?

At the end of this course, you will have a solid understanding of theoretical computer science. This will not only allow you to recognize some of the most challengi…

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Frequently asked questions

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.

Solving Hard Problems

This class teaches you about basic concepts in theoretical computer science—such as NP-completeness—and what they imply for…

Class Summary

This class teaches you about basic concepts in theoretical computer science -- such as NP-completeness -- and what they imply for solving tough algorithmic problems.

What Should I Know?

You should have a basic understanding of algorithms (such as CS215) and programming (such as CS101). No prior knowledge about theoretical computer science required!

What Will I Learn?

At the end of this course, you will have a solid understanding of theoretical computer science. This will not only allow you to recognize some of the most challenging algorithmic problems out there, but also give you powerful tools to deal with them in practice.

Syllabus

Unit 1: Challenging Problems

An introduction to tough problems and their analysis

Unit 2: Understanding Hardness

What we mean when a problem is “hard” and the concept of NP-completeness

Unit 3: Showing Hardness

Tools to let you recognize and prove that a problem is hard

Unit 4: Intelligent Force

Smart techniques to solve problems that should – theoretically – be impossible to solve

Unit 5: Sloppy Solutions

Gaining speed by accepting approximate solutions

Unit 6: Poking Around

Why randomness can be of help – sometimes. An introduction to complexity classes.

Unit 7: Ultimate Limits

Problems that no computer can ever solve. In theory.

Course Instructors

Sebastian Wernicke Instructor

Sebastian studied Bioinformatics at Universität Tübingen and holds a Ph.D. from Universität Jena in Germany, where his research focused on finding efficient algorithms for computationally hard problems on biological networks. After several years of strategic consulting for pharma companies and financial services, he's currently working with Seven Bridges Genomics, a big data bioinformatics startup. He is also well-known for his TED Talks, especially the one on the statistics of TED Talks..

Sarah Norell Assistant Instructor

Sarah Norell holds a PhD in Mathematics from the University of London, UK. She has lectured at the London School of Economics, University of Umeå and Mid-Sweden University and tutored at all ages. These experiences give her invaluable insight into the misconceptions students have about maths at many levels. This insight, plus Udacity tech, have allowed her to effectively teach maths online, like she's always wanted to.

Sean Bennett Assistant Instructor

Sean Bennett is a Course Architect at Udacity and is passionate about using the web to improve the quality of education available worldwide. Sean's background is in web programming, and he likes to dabble in functional web programming. When he's not working to improve education, Sean likes running, hiking, and preparing for the inevitable zombie apocalypse.

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There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.