This class teaches you about basic concepts in theoretical computer science -- such as NP-completeness -- and what they imply for solving tough algorithmic problems.
Why Take This Course?
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.
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.
Start your review of Intro to Theoretical Computer Science
Pacing of this course is all off. If you're gonna be doing this be prepared for a lot of frustrated evenings and hair-pulling frustration trying to get a grasp on what the lecturer is even trying to tell you.
Mauro Lacy completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
A good introduction to computational complexity, with some subtle theoretical concepts presented in a clear and amenable way. Stressing the importance of good theoretical foundations and the analysis of algorithms.