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AI theory, architecture, models
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Module 4
Unsupervised Learning and Representation
1
K-means Clustering and Silhouette Score Evaluation
Implement K-means clustering and evaluate its performance using the silhouette score
2
Hierarchical Clustering: Linkage Methods
Apply hierarchical clustering with different linkage methods
3
Implementing PCA from Scratch
Implement Principal Component Analysis (PCA) from scratch for dimensionality reduction
4
Visualizing High-Dimensional Data with t-SNE
Apply t-SNE for high-dimensional data visualization
5
Implementing GMMs with EM
Implement Gaussian Mixture Models (GMMs) using the Expectation-Maximization (EM) algorithm
Previous module
Classical and Ensemble Learning Algorithms
Next module
Deep Neural Network Fundamentals