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Machine Learning Syllabus Overview
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Module 3
Clustering with K-Means and GMMs
1
K-means: Assignment & Update Cycle
Describe the K-means algorithm's iterative process of cluster assignment and centroid updates.
K-means Clustering with scikit-learn and the Elbow Method
Implement K-means using scikit-learn and use the elbow method to estimate the optimal number of clusters (K).
3
K-means Limitations
Identify limitations of K-means, such as sensitivity to initial centroids and difficulty with non-convex clusters.
4
Introduction to Gaussian Mixture Models for Clustering
Describe Gaussian Mixture Models (GMM) as a probabilistic or 'soft' clustering method.
5
Understanding EM for GMMs: An Intuitive Dive
Explain the intuition behind the Expectation-Maximization (EM) algorithm used to fit GMMs.
6
GMM vs. K-Means: Soft vs. Hard Clustering in Scikit-learn
Fit a GMM using scikit-learn and compare its probabilistic assignments to K-means' hard assignments.
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Dimensionality Reduction with PCA
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Linear Regression and Regularization