Machine Learning: Unsupervised Learning and Clustering Techniques

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA

Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.

Clustering Methods

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

Supervised Learning: k-NN and Decision Trees

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

Clustering: Principles and Methods

Covers the principles and methods of clustering in machine learning, including similarity measures, PCA projection, K-means, and initialization impact.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Clustering: k-means

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

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.