1 / 9

Linear Algebra Basics & Probability Fundamentals for Machine Learning

This background text explores linear algebra concepts such as vectors in Euclidean space and probability fundamentals including Bayes' theorem, independence, mean, variance, and various distributions like Bernoulli, Binomial, and Normal (Gaussian).

lzepeda
Download Presentation

Linear Algebra Basics & Probability Fundamentals for Machine Learning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Background for Machine Learning (I) UsmanRoshan

  2. Linear algebra • Vector: • ordered collection of numbers • point in some Euclidean space • Examples: • x : (1, 2) • y : (3, 5) • z : (4, 1)

  3. Linear algebra • x : (1, 2), y : (3, 5),z : (4, 1) • y – x = (3-1,5-2)=(2,3) • x – z = (1-4,2-1)=(-3,1)

  4. Linear algebra • x : (1, 2), y : (3, 5),z : (4, 1) • Length of vector in Euclidean space • Length of x =

  5. Linear algebra

  6. Linear algebra θ

  7. Linear algebra θ

  8. Linear algebra

  9. Probability • Read Appendix of textbook Introduction to Machine by EthemAlpaydin • Bayes theorem • Independence • Mean • Variance • Distributions • Bernoulli • Binomial • Normal (Gaussian)

More Related