Social and Information Networks: Structure

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Related lectures (32)

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Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Supervised Learning Overview

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

Network Formation: Random Connection Models

Explores network formation through random connection models and node degree distribution.

Introduction to Networks and Brain Networks

Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.

Graph Mining: Social Networks and Data Analysis

Explores graph mining in social networks, covering data analysis and community detection.

Handling Networks: Graph Theory

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

Centrality and Hubs

Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.

Unsupervised Learning: Clustering

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

Inference for Stochastic Processes: Large Networks Analysis

Explores inference for stochastic processes, emphasizing large networks analysis and the need for new theories and methods.

Introduction to Image Classification

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