Cluster Analysis: Methods and Applications

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Related lectures (32)

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Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Clustering: k-means

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

Advanced Structure Discovery: Distance Metrics and Time Series Data

Explores clustering algorithms, distance metrics, and time series data analysis techniques.

Introduction to Image Classification

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

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Supervised Learning Overview

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

Genomic Data Analysis: Clustering and Survival

Explores genomic data clustering, survival analysis, gene identification, and statistical significance in cancer research.

Clustering: K-Means

Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.

Clustering: Unsupervised Learning

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

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.