Lecture

Generative Learning Algorithms

Related lectures (31)

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.

Explores Gaussian classifiers, texture classification, parameter estimation, supervised learning, and deep neural networks in image processing.

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.