Lectures related to Belief Propagation in Random Graphs

Statistical Analysis of Network Data: Structures and Models

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Causal Inference: Learning Graph Structures

Explores causal inference through learning graph structures for causal reasoning from observational data.

Graph Algorithms: Modeling and Traversal

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Information Theory: Basics

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

Algorithmes: introduction

Covers the basics of algorithms, problem-solving, and efficient resolution methods.

Graph Processing: Oracle Labs PGX

Covers graph processing with a focus on Oracle Labs PGX, discussing graph analytics, databases, algorithms, and distributed analytics challenges.

Entropy and the Second Law of Thermodynamics

Covers entropy, its definition, and its implications in thermodynamics.

Thermodynamics: Entropy and Ideal Gases

Explores entropy, ideal gases, and TDS equations in thermodynamics, emphasizing the importance of the Clausius inequality and the Carnot cycle.

Concurrent Programming: Theory to Practice

Explores concurrent programming theory and practice, covering system models, cache coherence, and graph processing.

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Linearity of Expectation: First Moment Method

Introduces Linearity of Expectation and the First Moment Method, explores probability theory problems like Buffon's Needle, and discusses transitive tournaments and Ham paths.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Phase Transitions: Understanding Thermodynamic Stability

Explores phase transitions in thermodynamics, focusing on stability criteria and real-world applications.

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Variational Formulation: Information Measures

Explores variational formulation for measuring information content and divergence between probability distributions.

Cayley Graphs

Covers Cayley graphs, generators, group examples, and graph structures.

Solving Parity Games in Practice

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Thermodynamic Identity: Entropy and Energy

Explores the thermodynamic identity, entropy-temperature relationship, and pressure definition, illustrating key principles with practical examples.

Information Measures: Entropy and Information Theory

Explains how entropy measures uncertainty in a system based on possible outcomes.