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Related lectures (31)

Detecting Communities in Random Graphs

Delves into detecting communities in random graphs, exploring fundamental laws, phase transitions, and spectral algorithms.

Random Field Ising Model on Graphs

Explores the Random Field Ising Model on random graphs, discussing belief propagation updates and population dynamics.

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Statistical Physics of Clusters

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

Phase Transitions: Connectivity in Random Graphs

Delves into phase transitions in random graphs, emphasizing the emergence of a giant component with varying connectivity.

Node Degree and Strength

Explores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.

Digital Humanities at Scale

Delves into Digital Humanities advancements from 2012 to 2032, focusing on deep learning technologies for historical document analysis.

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Centrality and Hubs

Explores centrality, hubs, eigenvectors, clustering coefficients, small-world networks, network failures, and percolation theory in brain networks.

Epidemics and Bootstrap Percolation in Square Lattice

Explores epidemics spread models and Bootstrap Percolation in square lattice networks, focusing on the Kolmogorov equation and probability generating functions.

Percolation: Random Graph Models

Covers percolation theory and random graph models, exploring connectivity thresholds and critical power for wireless networks.

Neural Signals and Connectomes

Explores neural signals, connectomes, graph theory, and multi-voxel pattern analysis in fMRI trials.

Node Degree and Strength

Explores brain node connectivity, node degree, strength, random networks, power law distributions, and the complexity of real networks.

Approximate Inference Algorithm

Covers the Chow-Liu algorithm for approximate inference and optimal tree approximation.

Transformations Affines MOOC: Analysis I MOOC: Analysis I (part 4) : Limit of a function, continuous functions

Covers the concept of affine transformations in functions, including shifting and compressing functions.

Decentralized ML: Collaborative Training & Causal Influence Structure

Explores collaborative training in decentralized ML and causal influence structure discovery.

Image Systems

Delves into image systems, physical connections, pattern propagation, and morphograph creation.

Information Theory: Basics

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

Statistical Analysis of Network Data Log in to Mediaspace to watch this video

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Latent Space and RDPG: Statistical Analysis of Network Data Log in to Mediaspace to watch this video

Covers latent space models, logistic regression, RDPG, and real-world networks.