Complexity Theory Running Time Analysis of Algorithms

Complexity Theory – Running Time Analysis of Algorithms, available at Free, has an average rating of 4.23, with 24 lectures, based on 2347 reviews, and has 32121 subscribers.

You will learn about Understand running time analysis To be able to analyze algorithms running times Understand complexity notations Understand complexity classes (P and NP) This course is ideal for individuals who are This course is meant for everyone who are interested in algorithms and want to get a good grasp on complexity theory It is particularly useful for 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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Summary

Title: Complexity Theory – Running Time Analysis of Algorithms

Price: Free

Average Rating: 4.23

Number of Lectures: 24

Number of Published Lectures: 18

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 18

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Understand running time analysis

To be able to analyze algorithms running times

Understand complexity notations

Understand complexity classes (P and NP)

Who Should Attend

This course is meant for everyone who are interested in algorithms and want to get a good grasp on complexity theory

Target Audiences

This course is meant for everyone who are interested in algorithms and want to get a good grasp on complexity theory

This course is about algorithms running time analysis and complexity theory. In order to be able to classify algorithms we have to define limiting behaviors for functions describing the given algorithm.

We will understand running times such as O(N*logN), O(N), O(logN) and O(1)– as well as exponential and factorial running time complexities.

Thats why big O, big Ω and big θ notations 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(polynomial) as well as NP(non-deterministic polynomial), NP-completeand NP-hardcomplexity classes.

Section 1 – Algorithms Analysis

how to measure the running time of algorithms

running time analysis with big O(ordo), big Ω (omega) and big θ(theta)notations

complexity classes

polynomial (P) and non-deterministic polynomial (NP) algorithms

Section 2 – Algorithms Analysis (Case Studies)

constant running time O(1)

linear running time O(N)

logarithmic running time O(logN)

quadratic running time complexity O(N*N)

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! Thanks for joining my course, let’s get started!

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! Thanks for joining my course, let’s get started!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Algorithms (Running Time) Analysis

Lecture 1: How to measure the running times of algorithms?

Lecture 2: Complexity theory illustration

Lecture 3: Complexity notations – big (O) ordo

Lecture 4: Complexity notations – big Ω (omega)

Lecture 5: Complexity notations – big (θ) theta

Lecture 6: Complexity notations – example

Lecture 7: Algorithm running times

Lecture 8: Complexity classes

Lecture 9: Analysis of algorithms – loops

Chapter 3: Running Time – Case Studies

Lecture 1: Case study – O(1)

Lecture 2: Case study – O(logN)

Lecture 3: Case study – O(N)

Lecture 4: Case study – O(N*N)

Lecture 5: Case study – O(2^N)

Chapter 4: Algorhyme FREE Algorithms Visualizer App

Lecture 1: What is Algorhyme?

Lecture 2: Algorhyme – Algorithms and Data Structures

Chapter 5: Course Materials (DOWNLOADS)

Lecture 1: Course materials

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 39 votes

2 stars: 41 votes

3 stars: 340 votes

4 stars: 881 votes

5 stars: 1046 votes

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