Generative Learning Algorithms

Related lectures (31)

Page 1 of 4

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Explores generative learning, class priors, and maximum likelihood estimation in classification models.

Covers supervised learning through likelihood maximization to find optimal parameters.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Covers Gaussian discriminant analysis, log-likelihood, supervised learning, and logistic regression.

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.