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.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Mauro Lacycompleted 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.