Single Proteins Maps: Sabrina Simoncelli

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Related lectures (32)

Page 1 of 4

Explores graph metrics and statistical analysis in network clustering, including ERGMs application in sociology and asymptotics.

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Supervised Learning Overview

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

Statistical Consequences of Clustering

Covers the statistical consequences of clustering and the complexities of return level estimation in extreme events.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Expectation Maximization and Clustering

Covers the Expectation Maximization algorithm and clustering techniques, focusing on Gibbs Sampling and detailed balance.

Clustering Algorithms: K-Means vs Spectral Clustering

Compares K-Means and Spectral Clustering algorithms, highlighting their differences and practical applications in clustering student behaviors.

Block Models: Continued Analysis

Explores the stochastic blockmodel, spectral clustering, and non-parametric understanding of blockmodels, emphasizing metrics for comparing graph models.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.