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.

Expected Learning

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

Lesson 1: Challenging Problems

An introduction to tough problems and their analysis

Lesson 2: Understanding Hardness

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

Lesson 3: Showing Hardness

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

Lesson 4: Intelligent Force

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

Lesson 5: Sloppy Solutions

Gaining speed by accepting approximate solutions

Lesson 6: Poking Around

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

Lesson 7: Ultimate Limits

Problems that no computer can ever solve. In theory.

Required Knowledge

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