Decision Making Under Uncertainty

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Related lectures (32)

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Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Supervised Learning Overview

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

Introduction to Image Classification

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

K-means Algorithm

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

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

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Neural Networks: Training and Optimization

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

Clustering: Theory and Practice

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

Unsupervised Learning: Clustering Methods

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

Clustering: Unsupervised Learning

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