[Statistics 110] 29. Law of Large Numbers and Central Limit Theorem
Law of Large Numbers, Central Limit Theorem
Math
Foundation of everything.
[Statistics 110] 28. Inequalities
Inequalities
[Statistics 110] 27. Conditional Expectation given an R.V.
Conditional Expectation given an R.V.
[Statistics 110] 26. Conditional Expectation Continuted
Money Envelope Problem, Patterns in Coin Flips, Conditional Expectation
[Statistics 110] 25. Order Statistics and Conditional Expectations
Bank-Post office Example, Order Statistics, Conditional Expectations
[Statistics 110] 24. Gamma distribution and Poisson process
Gamma distribution and Poisson process
[Statistics 110] 23. Beta distribution
Beta distribution
[Statistics 110] 22. Transformations and Convolutions
Transformations and Convolutions
[Statistics 110] 21. Covariance and Correlation
Covariance and Correlation
[Statistics 110] 20. Multinomial and Cauchy
Multinomial and Cauchy Interview Problem
[Statistics 110] 19. Joint, Conditional, and Marginal Distributions
Joint, Conditional, and Marginal Distributions, 2D-LOTUS, Chicken Egg Problem
[Statistics 110] 18. MGFs Continued
MGFs, Joint Distribution
[Statistics 110] 17. Moment Generating Functions
Memoryless Property, Moment Generating Functions, Laplace Rule of Succession
[Statistics 110] 16. Exponential Distribution
Exponential Distribution
[Statistics 110] 15. Midterm Review
Coupon Collector Problem, Universality, Symmetry, Swapping Success and Failure, Email Problem
[Statistics 110] 14. Location, Scale, and LOTUS
Location and Scale of Normal Distribution, Variations, LOTUS
[Statistics 110] 13. Normal Distribution
Uniform Distribution, Independenc of random variables, Normal Distribution
[Statistics 110] 12. Discrete vs. Continuous, the Uniform
Continuous R.V, Uniform Distribution
[Statistics 110] 11. The Poisson distribution
The Poisson distribution, Poisson Paradigm
[Statistics 110] 10. Expectation Continued
Expectation, Negative Binomial, Putnam, St.Petersburg Paradox
[Statistics 110] 09. Expectation, Indicator Random Variables, Linearity
Expectation, Indicator Random Variables, Linearity, Geometric Distribution
[Statistics 110] 08. Random Variables and Their Distributions
Binomial Distribution and Random Variables, CDF, and PMF
[Statistics 110] 07. Gambler’s Ruin and Random Variables
Gambler’s Ruin and Random Variables
[Statistics 110] 06. Monty Hall, Simpson’s Paradox
Monty Hall, Simpson’s Paradox
[Statistics 110] 05. Conditioning Continued, Law of Total Probability
Law of Total Probability, Biohazards, Conditional Independence
[Statistics 110] 04. Conditional Probability
Independent, Newton-Pepy’s Problem, Conditional Probability
[Statistics 110] 03. Birthday Problem, Properties of Probability
Birthday Problem, Properties of Probability, deMontMort’s problem
[Statistics 110] 02. Story Proofs, Axioms of Probability
Story Proofs, Axioms of Probability
[Statistics 110] 01. Probability and Counting
Naive definition of probability, Multiplication rule, Sampling table
[Linear Algebra for AI] 21. 고유값 분해와 특이값 분해의 응용
Application of Eigen Decomposition and SVD
[Linear Algebra for AI] 20. 특잇값 분해 Ⅱ
How to get SVD
[Linear Algebra for AI] 19. 특잇값 분해 Ⅰ
Perspectives of Singular Value Decomposition
[Linear Algebra for AI] 18. 고유값 분해와 선형변환
Eigen Decomposition on the perspective of Linear transformation
[Linear Algebra for AI] 17. 대각화
Diagonalization
[Linear Algebra for AI] 16. 특성방정식
Characteristic Eqaution
[Linear Algebra for AI] 15. 영공간과 직교여공간
Nullspace and orthogonal complement
[Linear Algebra for AI] 14. 고유벡터와 고유값
Eigenvector and Eigenvalue
[Linear Algebra for AI] 13. 그람-슈미트 직교화와 QR 분해
Gram-Schmidt Orthogonalization & A=QR
[Linear Algebra for AI] 12. Orthogonal Projection Ⅱ
Orthogonal Projection & Linear Transformation
[Linear Algebra for AI] 11. Orthogonal Projection Ⅰ
Orthonormal sets, Orthogonal Projection
[Linear Algebra for AI] 9. Least Squares와 그 기하학적 의미
Least Squares와 그 기하학적 의미
[Linear Algebra for AI] 8. Least Squares Problem 소개
Least Square Solution
[Linear Algebra for AI] 7. 전사함수와 일대일함수
Onto와 One-To-One, 신경망에서의 의미
[Linear Algebra for AI] 6. 선형변환
Linear transformation
[Linear Algebra for AI] 5. 부분공간의 기저와 차원
Subspace, basis, dimension, column space, rank
[Linear Algebra for AI] 4. 선형독립과 선형종속
Linear Independent
[Linear Algebra for AI] 3. 선형결합
선형결합, Span, Matrix의 곱을 대하는 네 가지 관점
[Linear Algebra for AI] 2. 선형방정식과 선형시스템
Scalar, Vector, Matrix의 개념과 계산법
[Linear Algebra for AI] 1. 선형대수의 기초
Scalar, Vector, Matrix의 개념과 계산법