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Background for Machine Learning (I)

Delve into the fundamentals of linear algebra in machine learning. Explore vectors, Euclidean spaces, and vector operations like addition and subtraction. Learn the essentials of probability theory including Bayesian theorem, independence, and common distributions.

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Background for Machine Learning (I)

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  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)

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