Network Analysis: Methods and Applications

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Related lectures (32)

Page 2 of 4

Handling Networks: Graph Theory

Covers the fundamentals of handling networks and centrality measures in graph theory.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Super-resolution Network Analysis: SuperNet

Introduces SuperNet, a software for super-resolution network analysis and quantification of single molecule clusters, covering motivation, challenges, methodology, features, and proposed methods.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Social Network Analysis: Modularity Measure

Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.

Graph Mining: Social Networks Analysis

Explores graph mining in social networks, covering modularity algorithms and community detection.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Statistical Analysis of Network Data: Structures and Models

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

The Role of Symmetries

Delves into symmetries in physics, covering group theory, perturbation, and quantification.