statistical machine learning

Statistical learning is a science about probabilistic statistical models built by computers based on data and using the models to predict and analyze data. Statistical learning has also become statistical machine learning.

• Chapter 1：Probability basis (42 minutes and 55 seconds)

• Chapter 2：Random variable 1 (42 minutes and 21 seconds)

• Chapter 3：Random variable 2 (44 minutes and 28 seconds)

• Chapter 4：Gaussian distribution (41 minutes and 39 seconds)

• Chapter 5：Gaussian distribution example (42 minutes and 32 seconds)

• Chapter 6：continuous distribution (38 minutes and 58 seconds)

• Chapter 7：jeffrey prior (40 minutes and 44 seconds)

• Chapter 8：scale mixture pisribarin (41 minutes and 48 seconds)

• Chapter 9：statistical interence (35 minutes and 57 seconds)

• Chapter 10：Laplace transform (45 minutes and 17 seconds)

• Chapter 11：Multivariate distribution definition (35 minutes and 18 seconds)

• Chapter 12：probability transformation (34 minutes and 31 seconds)

• Chapter 13：Jacobian (34 minutes and 2 seconds)

• Chapter 14：Wedge production (34 minutes and 32 seconds)

• Chapter 15：Wishart distribution (38 minutes and 31 seconds)

• Chapter 16：multivariate normal distribution (38 minutes and 39 seconds)

• Chapter 17：Statistics (37 minutes and 44 seconds)

• Chapter 18：Matrix element beta distribution (14 minutes and 36 seconds)

• Chapter 19：Conjugate transcendental properties (21 minutes and 15 seconds)

• Chapter 20：sufficient statistics (40 minutes and 9 seconds)

• Chapter 21：exponential value distribution (37 minutes and 15 seconds)

• Chapter 22：Entropy (42 minutes and 37 seconds)

• Chapter 23：KL distance (37 minutes and 43 seconds)

• Chapter 24：Properties (23 minutes and 59 seconds)

• Chapter 25：Probability inequality 1 (42 minutes and 54 seconds)

• Chapter 26：Probability inequality 2 (36 minutes and 3 seconds)

• Chapter 27：Probability inequality 1 (39 minutes and 20 seconds)

• Chapter 28：Probability inequality 2 (34 minutes and 59 seconds)

• Chapter 29：Probability inequality 3 (35 minutes and 45 seconds)

• Chapter 30：John's lemma (27 minutes and 44 seconds)

• Chapter 31：probability inequality (38 minutes and 18 seconds)

• Chapter 32：random projection (37 minutes and 20 seconds)

• Chapter 33：Stochastic Convergence-Concept (42 minutes and 54 seconds)

• Chapter 34：Stochastic Convergence-Properties (27 minutes and 41 seconds)

• Chapter 35：Stochastic Convergence-Application (24 minutes and 4 seconds)

• Chapter 36：EM algorithm 1 (43 minutes and 44 seconds)

• Chapter 37：EM algorithm 2 (39 minutes and 23 seconds)

• Chapter 38：EM algorithm 3 (27 minutes and 3 seconds)

• Chapter 39：Bayesian Classification (38 minutes and 20 seconds)

• Chapter 40：Markov Chain Monte carlo1 (44 minutes and 20 seconds)

• Chapter 41：Markov Chain Monte carlo2 (20 minutes and 2 seconds)