Lectures related to Unsupervised Learning: Clustering

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: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Clustering: Unsupervised Learning

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

Nearest Neighbor Rules: Part 2

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

Clustering: Unsupervised Learning

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

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 & LDA

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

Machine Learning Fundamentals

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

Clustering Methods: K-means and DBSCAN

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

Machine Learning: Basics and Applications

Covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Decision Making Under Uncertainty

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

Clustering: Dimensionality Reduction

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Unsupervised Machine Learning: Clustering Basics

Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Unsupervised Learning: Clustering Methods

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

K-means Algorithm Log in to Mediaspace to watch this video

Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.

Unsupervised Learning: PCA & K-means Log in to Mediaspace to watch this video

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Dimensionality Reduction: PCA and LDA Log in to Mediaspace to watch this video

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

Unsupervised Learning: Clustering & Dimensionality Reduction Log in to Mediaspace to watch this video

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.