Brief Introduction
In the computational world of AI you will often encounter enormous amounts of data that needs to be processed. Often, the data volume will be so large that you will need to use some form of data reduction technique. Eigen-concepts are a big part of the mathematical background needed to understand a useful data reduction tools, called Principal Component Analysis (PCA).Course Summary
Learn about eigenvectors and eigenvalues, which are important concepts in linear algebra that have applications in various fields such as physics, engineering, and computer science.Key Learning Points
- Understand the definition and properties of eigenvectors and eigenvalues
- Learn how to calculate eigenvectors and eigenvalues for matrices
- Explore the applications of eigenvectors and eigenvalues in different fields
Job Positions & Salaries of people who have taken this course might have
- USA: $113,000
- India: ₹1,250,000
- Spain: €38,000
- USA: $113,000
- India: ₹1,250,000
- Spain: €38,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €36,000
- USA: $113,000
- India: ₹1,250,000
- Spain: €38,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €36,000
- USA: $85,000
- India: ₹1,000,000
- Spain: €32,000
Related Topics for further study
Learning Outcomes
- Ability to calculate eigenvectors and eigenvalues for matrices
- Theoretical understanding of eigenvectors and eigenvalues
- Application of eigenvectors and eigenvalues in various fields
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of linear algebra
- Familiarity with matrix operations
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Video lectures
- Quizzes
Similar Courses
- Introduction to Linear Algebra
- Matrix Methods for Data Analysis, Signal Processing, and Machine Learning
Related Education Paths
Notable People in This Field
- Gilbert Strang
- Stephen Boyd
Related Books
Description
Learn how to calculate eigenvalues and eigenvectors and why they are important for AI applications.Requirements
- To easily understand this class you will need to have mathematical background in Linear Algebra. Refresh your memory or go over the topics of Linear transformation , Determinants And a System of linear equations before beginning. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Vectors Linear Transformation lesson 2 Definitions and Calculations Characteristic Equation of a matrix Eigenvalues Eigenvectors lesson 3 Why is the relevant to Machine Learning? Principle Component Analysis (PCA)
Summary of User Reviews
Learn about eigenvectors and eigenvalues in this comprehensive course offered by Udacity. Users have found the course to be informative and easy to understand, with a focus on real-world applications.Key Aspect Users Liked About This Course
The course provides a thorough understanding of eigenvectors and eigenvalues and their practical applications.Pros from User Reviews
- The course is well-structured and easy to follow
- The instructor provides clear explanations and examples
- The course includes real-world applications of eigenvectors and eigenvalues
- The course is suitable for both beginners and advanced learners
Cons from User Reviews
- The course may be too basic for those with advanced knowledge of linear algebra
- There is limited interaction with the instructor
- The course does not provide enough practice problems
- The course does not cover some advanced topics related to eigenvectors and eigenvalues
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