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Generative Learning & Model Properties

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Related lectures (52)

Logistic Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Estimators and Confidence Intervals Log in to Mediaspace to watch this video

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

Maximum Likelihood Theory & Applications Log in to Mediaspace to watch this video

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

MLE Applications: Binary Choice Models Log in to Mediaspace to watch this video

Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.

Logistic Regression: Interpretation & Feature Engineering Log in to Mediaspace to watch this video

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Maximum Likelihood Estimation: Theory Log in to Mediaspace to watch this video

Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.

Logistic Regression: Classification Log in to Mediaspace to watch this video

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Supervised Learning: Classification Algorithms Log in to Mediaspace to watch this video

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

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Image Processing II: Gaussian Classifiers and Neural Networks Log in to Mediaspace to watch this video

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

Binary Response: Link Functions Log in to Mediaspace to watch this video

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.

Latent Space Models: Inference and Applications Log in to Mediaspace to watch this video

Explores latent space models, network representations, spectral decompositions, and parameter estimation methods.

Linear and Logistic Regression Log in to Mediaspace to watch this video

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

Linear Models: Part 2 Log in to Mediaspace to watch this video

Covers linear models, binary and multi-class classification, and logistic regression with practical examples.

Machine Learning Fundamentals: Regularization and Cross-validation Log in to Mediaspace to watch this video

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Efficient Machine Learning via Data Summarization Log in to Mediaspace to watch this video

Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.

Feature Engineering: Polynomial Regression Log in to Mediaspace to watch this video

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Generalized Linear Models Log in to Mediaspace to watch this video

Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.

Linear Models & k-NN Log in to Mediaspace to watch this video

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Elements of Statistics: Probability, Distributions, and Estimation Log in to Mediaspace to watch this video

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

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