Unsupervised Behavior Clustering

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Page 2 of 4

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.

Unsupervised Learning: Clustering & Dimensionality Reduction

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

Clustering: K-means & LDA

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

Principal Component Analysis: Dimensionality Reduction

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

Clustering Methods: K-means and DBSCAN

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

Unsupervised Learning: Clustering Methods

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

Feature Extraction & Clustering Methods

Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.

Clustering Methods

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

Unsupervised Learning: PCA & K-means

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