Free Course
Computability, Complexity & Algorithms
by
The Power and Limitation of Algorithms
Nanodegree Program
Become a Machine Learning Engineer
Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Deep Learning.
Accelerate your career with the credential that fast-tracks you to job success.
About this Course
This class is offered as CS6505 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.
In this course, we will ask the big questions, “What is a computer? What are the limits of computation? Are there problems that no computer will ever solve? Are there problems that can’t be solved quickly? What kinds of problems can we solve efficiently and how do we go about developing these algorithms?” Understanding the power and limitations of algorithms helps us develop the tools to make real-world computers smarter, faster and safer.
Course Cost
Free
Skill Level
advanced
Included in Product
Rich Learning Content
Interactive Quizzes
Taught by Industry Pros
Self-Paced Learning
Student Support Community
Join the Path to Greatness
This free course is your first step towards a new career with the Become a Machine Learning Engineer Program.
Free Course
Computability, Complexity & Algorithms
Enhance your skill set and boost your hirability through innovative, independent learning.
Course Leads
Charles Brubaker
Instructor
Lance Fortnow
Instructor
Hariharan Venkateswaran
Instructor
What You Will Learn
lesson 1
Computability
- Languages & Countability
- Turing Machines
- The Church-Turing Thesis
lesson 2
Complexity
- P and NP
- NP-Complete Problems
- The Golden Ticket
lesson 3
Algorithms
- Dynamic Programming
- Fast Fourier Transform
- Maximum Flow
Prerequisites and Requirements
Students are expected to have a solid grasp of the basics of discrete mathematics. Discrete Mathematics and Its Applications by Ken Rosen provides an excellent background for this course.
If you answer “no” to any of the following questions, it may be beneficial to acquire background knowledge concurrently or prior to taking the course.
- Can you show that the sum of the first n numbers is n(n+1)/2? Can you give the proof as an induction on n?
- Can you give an O(n log n) algorithm for sorting n numbers?
- Can you describe the difference between breadth-first and depth-first search?
- Given an nxn matrix A and an n-dimensional vector b, can you give a polynomial-time algorithm to find a vector x such that Ax=b?
See theTechnology Requirements for using Udacity.
Why Take This Course
You will learn a wealth of tools and techniques that will help you recognize when problems you encounter in the real-world are intractable and when there an efficient solution. This can save you countless hours that otherwise would have been spent on a fruitless endeavor or in re-inventing the wheel.