Intro to Theoretical Computer Science
via Udacity
Overview
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.
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.
Taught by
Sebastian Wernicke
Related Courses
-
NP-Complete Problems
University of California, San Diego
-
Intro to Algorithms
2.7 -
Computability, Complexity & Algorithms
Georgia Institute of Technology
5.0 -
Algorithms and Data Structures
University of California, San Diego
-
Introduction to Graduate Algorithms
Georgia Institute of Technology
-
Sparse Representations in Signal and Image Processing: Fundamentals
Technion - Israel Institute of Technology
5.0
Reviews
5.0 rating, based on 1 reviews
-
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.
Never Stop Learning!
