Lectures related to Clustering high-dimensional data

Unsupervised Behavior Clustering

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

Statistical Analysis of Networks: Link Prediction and Biclustering

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

Clustering: Unsupervised Learning

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

Single cell RNA-sequencing: Methods and Applications

Explores single cell RNA-sequencing methods, applications, tissue studies, and data clustering.

Clustering Techniques: K-means and DBSCAN

Explores k-means and DBSCAN clustering techniques, covering data point assignment and classification types.

Resource Management: Static vs Resource Containers in YARN

Explores static resource allocation and resource containers in distributed computing frameworks, emphasizing the flexibility and efficiency brought by YARN.

Decision Making Under Uncertainty

Explores decision-making under uncertainty, focusing on Kilian Schindler's posthumous PhD thesis on scalable stochastic optimization and scenario reduction.

Unsupervised Learning: Clustering Methods

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

Extensions to Exchangeability: Statistical Analysis of Network Data

Covers extensions to exchangeability in network data analysis, including graphon frameworks and edge exchangeability.

The Gamma Parallel Database Machine: Data Clustering and Declustering

Explores The Gamma Parallel Database Machine, data clustering, and declustering tradeoffs.

Introduction to Clustering: Methods and Applications

Covers the fundamentals of clustering in unsupervised learning and its practical applications.

Animal Behavior Dynamics: Hidden Insights and Predictive Models

Explores hidden dynamics in animal behavior, emphasizing the importance of short timescale behavior for predicting long timescale dynamics.

Support Vector Clustering: SVC

Introduces Support Vector Clustering (SVC) using a Gaussian kernel for high-dimensional feature space mapping and explains its constraints and Lagrangian.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Semi-supervised Clustering: Transductive SVM

Explores Transductive Support Vector Machine for semi-supervised clustering, aiming for zero error on labeled points and well-separated unlabeled points.

Single cell RNA-sequencing: Methods and Data Analysis

Covers the process of single cell RNA-sequencing and clustering data.

Statistical Signal & Data Processing

Explores Statistical Signal & Data Processing fundamentals, tools, and applications in various fields.

Machine Learning: Types and Applications

Covers the types of machine learning, including supervised, unsupervised, and reinforcement learning.

Gaussian Mixture Clustering: Expectation-Maximization Algorithms

Explores the Expectation-Maximization algorithm for Gaussian Mixture Clustering and its challenges and practical implementation.