Complexity Theory Basics
4.0 (745 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
14,724 students enrolled

Complexity Theory Basics

Asymptotic complexity, complexity theory, running times, complexity classes
4.0 (745 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
14,724 students enrolled
Created by Holczer Balazs
Last updated 4/2019
English
English [Auto-generated]
Price: Free
This course includes
  • 1.5 hours on-demand video
  • 2 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What you'll learn
  • Understand running time
  • Analyze algorithms

  • Understand complexity notations

Requirements
  • Basic programming concepts
Description

This course is about algorithms running times and complexity theory. In order to be able to classify algorithms we have to define limiting behaviors for functions describing the given algorithm. Thats why big O, big theta and big omega came to be. We are going to talk about the theory behind complexity theory as well as we are going to see some concrete examples. Then we will consider complexity classes including P as well as NP. These concepts are fundamental if we want to have a good grasp on data structures and graph algorithms, so these topics are definitely worth considering. Hope you will like it!

Who this course is for:
  • This course is meant for everyone who are interested in algorithms and want to get a good grasp on complexity theory
Course content
Expand all 16 lectures 01:21:41
+ Algorithms Analysis
13 lectures 01:19:58
Complexity theory basics
06:38
Complexity theory illustration
03:23
Complexity notations - big ordo
07:17
Complexity notations - big omega
05:12
Complexity notations - big theta
01:45
Complexity notations - example
09:10
Algorithm running times
09:43
Complexity classes
07:14
Analysis of algorithm - loops
04:32
Case study O(N) - linear search
09:23
Case stude O(logN) - binary search
05:10
Case study O(N*N) - bubble sort
06:29
Measuring running times
04:02