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Video Book:Machine Learning
Machine Learning
Stochastic Modeling
- Basic Probability Theory
- Counting measure
- Measure (mathematics)
- Probability measure
- Probability mass function
- Lebesgue measure
- Contingency table
- Conditional independence
- Expected value
- Law of the unconscious statistician
- Precision (statistics)
- Covariance matrix
- Partial correlation
- Probability Space
- Random variable
- Multivariate random variable
- Probability space
- Sigma-algebra
- Countable set
- Closure (mathematics)
- Field of sets
- Borel Sets
- Exponential family
- Graph Theory
- Clique (graph theory)
- Cartesian product of graphs
- Moral graph
- Junction tree algorithm
- Markov blanket
- Markov network
- Set Theory
- Subset and superset
- Disjoint sets
- Infimum and supremum
- Partially ordered set
- Statistics
- Calculus of Variation
- Gradient
- Lagrange multiplier
- Information Theory
- Mutual information
- RBM
- Sufficient statistic
- Measure Theory
- Pushforward measure
- Bayesian probability
- Marginal distribution
- Conditional probability
- Posterior probability
- Likelihood function
- Bayes' theorem
- Law of total probability
Source of the article : Wikipedia
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