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
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how to measure the running time of algorithms
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running time analysis with big O(ordo), big Ω (omega) and big θ(theta)notations
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complexity classes
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polynomial (P) and non-deterministic polynomial (NP) algorithms
Section 2 – Algorithms Analysis (Case Studies)
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constant running time O(1)
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linear running time O(N)
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logarithmic running time O(logN)
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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
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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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