Lectures related to Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Specification Testing and Machine Learning

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Choice models with latent variables: Modeling latent concepts MOOC: Selected Topics on Discrete Choice

Explores choice models with latent variables and their estimation process based on likelihood integration.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Logistic Regression: Vegetation Prediction

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

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.

Applied Machine Learning

Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

Generalized Linear Regression: Classification

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

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.

Supervised Learning Fundamentals Log in to Mediaspace to watch this video

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

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.

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

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

Model Complexity and Overfitting in Machine Learning Log in to Mediaspace to watch this video

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Decision Trees and Random Forests: Concepts and Applications Log in to Mediaspace to watch this video

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.