Complexity Theory Basics
4.4 (283 ratings)
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Complexity Theory Basics

Asymptotic complexity, complexity theory, running times, complexity classes
4.4 (283 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
4,449 students enrolled
Created by Holczer Balazs
Last updated 8/2017
English
Price: Free
Includes:
  • 1.5 hours on-demand video
  • 2 Articles
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Understand running time
  • Analyze algorithms
  • Understand complexity notations
View Curriculum
Requirements
  • Basic programming concepts
Description

This video 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 have 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 wel as NP. These concepts are fundamental if we want to have a good grasp ondata structures and graph algorithms, so these topics are definitely worth considering. Hope you will like it!

Who is the target audience?
  • This course is meant for everyone who are interested in algorithms and want to get a good grasp on complexity theory
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Curriculum For This Course
16 Lectures
01:21:14
+
Introduction
1 Lecture 01:11
Introduction
01:11
+
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
+
Course Data
2 Lectures 00:05
Course data
00:03

Discounted coupons for other courses
00:02
About the Instructor
Holczer Balazs
4.4 Average rating
3,944 Reviews
38,901 Students
24 Courses
Software Engineer

Hi!

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

Take a look at my website and join my email list if you are interested in these topics!