Lectures related to Determining the number of clusters in a data set

Supervised Learning Overview

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

Clustering Algorithms: K-Means vs Spectral Clustering

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

Unsupervised Learning: Clustering Methods

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

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Dimensionality Reduction: PCA and LDA

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

K-Means Clustering: Basics and Applications

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

Clustering: K-means & LDA

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

K-means Clustering: Initialization and Image Segmentation

Explores k-means clustering, emphasizing initialization and image segmentation.

Evaluation for Clustering

Covers the evaluation of clustering methods, including K-means clustering and the use of evaluation metrics to determine the optimal number of clusters.

Image Segmentation: K-means and Color Spaces

Explores image segmentation using K-means clustering and discusses the impact of different color spaces.

K-means Clustering: Assignment and Update Steps

Explains the assignment and update steps in K-means clustering, loss function minimization, and distance metric effects.

Single cell RNA-sequencing: Methods and Applications

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

Clustering: Unsupervised Learning

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

Gaussian Mixture Clustering: Expectation-Maximization Algorithms

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

Recommender Systems and Structure Discovery

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Graph Coloring: Random vs Symmetrical

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

Structure Discovery: Tracing Student Knowledge

Introduces Bayesian Knowledge Tracing, Additive Factors Model, and clustering algorithms for tracing student knowledge and discovering structures.

Extensions to Exchangeability: Statistical Analysis of Network Data

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

Neural Networks: Training and Optimization

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.