Lectures related to Latent and observable variables

Causal Discovery: Latent Variable Models

Explores causal discovery using latent variable models, emphasizing the challenges and solutions in inferring causal relationships from non-Gaussian data.

Discrete Choice Analysis

Explores the integration of machine learning into discrete choice models, emphasizing the importance of theory constraints and hybrid modeling approaches.

Choice models with latent variables: Modeling latent concepts

Explores choice models with latent variables, including indirect measurements and psychometric indicators.

Latent variables in choice models: Optima case study

Explores the Optima case study, analyzing attitudes and mode choice.

Understanding Preference Elicitation

Explores preference elicitation, latent variables, and the impact of external events on decision-making.

Latent Factor Analysis: Movie Genre Classification

Explores latent factor analysis for movie genre classification based on male versus female leads.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Factor Models: Latent Variables and Asset Pricing

Covers Factor Models, including PCA, asset pricing, factor investing, GMM, and Fama-McBeth estimation.

Latent Space Models: Inference and Applications

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

Recommender Systems: Overview and Methods

Explores the evolution of recommenders, collaborative filtering, Netflix Prize, model training, and optimization techniques.

Choice models with latent variables: Modeling latent concepts

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

Knowledge Tracing: Student Learning and Latent Variables

Explores Bayesian Knowledge Tracing and latent variables for tracing student knowledge and making predictions.

Learning Latent Models in Graphical Structures

Explores learning latent models in graphical structures, focusing on scenarios with incomplete samples and introducing the notion of distance among variables.

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Latent Variable Models

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

Introduction to ML for Behavioral Data

Covers machine learning approaches for personalization and their real-world application, emphasizing homework, projects, and feedback.

Recent Advances in Structural Learning for Graphical Models

Covers recent advances in structural learning for graphical models, including Gaussian models, mixed models, and extreme events.

Front Door Criterion: Adjustment Formula

Explores the front door criterion for valid adjustment sets in causal inference.

Interactive Lecture HMM: Definitions and Topologies

Explores Hidden Markov Models definitions, topologies, learning process, and current research trends.