Maximum Likelihood Estimation: Theory

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Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Binary Choice Models and Time Series Analysis

Explores binary choice models like probit and logit, as well as univariate time series analysis with ARIMA models for forecasting economic variables.

Bayesian Estimation: Overview and Examples

Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.

Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Maximum Likelihood Estimation: Econometrics

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

Explores maximum likelihood estimation, logistic regression, covariance estimation, and support vector machines for classification problems.

Binary Responses: Link Functions and GLMs

Explores link functions for binary responses and the impact of sparseness on model interpretability.

Elements of Statistics: Probability, Distributions, and Estimation

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