Lectures related to Graphical model

Explores the topological Künneth Theorem, emphasizing commutativity and homotopy equivalence in chain complexes.

Explores counterfactuals in SEMs and D-Separation in graphical models.

Explores the Chow-Liu Algorithm for structure learning and optimizing distributions through spanning trees and K-L divergence.

Belief Propagation: Key Methods and Analysis

Covers Belief Propagation, a key method for both analysis and algorithm.

Acyclic Models: Cup Product and Cohomology

Covers the cup product on cohomology, acyclic models, and the universal coefficient theorem.

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.

Physarum can compute shortest paths

Explores how Physarum Polycephalum can compute shortest paths in a directed graph model.

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.

Statistical Physics of Clusters

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Graphical Models: Probability Distributions and Factor Graphs

Covers graphical models for probability distributions and factor graphs representation.

Introduction to Proximal Operators

Introduces proximal operators and conditional gradient methods for composite convex minimization problems in data optimization.

Machine Learning for Behavioral Data

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.

Multivariate Statistics: Introduction and Methods

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Learning from Probabilistic Models

Delves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.

Text Models: Word Embeddings and Topic Models

Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.

Named Entity Recognition: Applications and Techniques

Explores Named Entity Recognition, its uses, techniques, and applications in information extraction.

Graphical Models: Joint Probability Distribution

Covers the concept of graphical models and joint probability distributions.

Recommender Systems: Text Classification & Naïve Bayes

Explores text classification using Naïve Bayes in content-based recommenders.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Linear Regression: Regularization Overview

Explores linear regression fundamentals, emphasizing the importance of regularization techniques to enhance model performance.