Maximum Likelihood Estimation: Linear Models

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Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

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Explores Maximum Likelihood Estimation, linear models, logistic regression, and Support Vector Machines.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Supervised Learning Essentials

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

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Introduces regularization techniques to prevent overfitting in machine learning models.

Maximum Likelihood Estimation: Multivariate Statistics

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

MLE Applications: Binary Choice Models

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

Generalized Linear Regression: Classification

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Maximum Likelihood Theory & Applications

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