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
Timeline
Approx. 0
Skill Level
Advanced
Included in Course
  • Icon course 01 3edf6b45629a2e8f1b490e1fb1516899e98b3b30db721466e83b1a1c16e237b1 Rich Learning Content

  • Icon course 04 2edd94a12ef9e5f0ebe04f6c9f6ae2c89e5efba5fd0b703c60f65837f8b54430 Interactive Quizzes

  • Icon course 02 2d90171a3a467a7d4613c7c615f15093d7402c66f2cf9a5ab4bcf11a4958aa33 Taught by Industry Pros

  • Icon course 05 237542f88ede3178ac4845d4bebf431ddd36d9c3c35aedfbd92e148c1c7361c6 Self-Paced Learning

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Join the Path to Greatness

This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.

Free Course

Computability, Complexity & Algorithms

by Georgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

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Course Leads

  • Charles Brubaker
    Charles Brubaker

    Instructor

  • Lance Fortnow
    Lance Fortnow

    Instructor

  • Hariharan Venkateswaran
    Hariharan Venkateswaran

    Instructor

What You Will Learn

Lesson 1

Computability

  • Languages & Countability
  • Turing Machines
  • The Church-Turing Thesis
Lesson 1

Computability

  • Languages & Countability
  • Turing Machines
  • The Church-Turing Thesis
Lesson 2

Complexity

  • P and NP
  • NP-Complete Problems
  • The Golden Ticket
Lesson 2

Complexity

  • P and NP
  • NP-Complete Problems
  • The Golden Ticket
Lesson 3

Algorithms

  • Dynamic Programming
  • Fast Fourier Transform
  • Maximum Flow
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.

  1. 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?
  2. Can you give an O(n log n) algorithm for sorting n numbers?
  3. Can you describe the difference between breadth-first and depth-first search?
  4. 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 the Technology 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.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals

Thanks for your interest!

We'll be in touch soon.

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