Machine Learning Basics

Related lectures (31)

Page 3 of 4

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

Explores clustering concepts, emphasizing intra-class similarity and cost-effective methods.

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

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

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

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

Covers the basics of machine learning, focusing on supervised learning algorithms and real-world applications.

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

Delves into deep learning for image and object classification in IoT systems, including clustering techniques and privacy concerns.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.