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Introduction to Data Science (DS 215)
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Module 10
Classification, Clustering, and Mixture Models
1
Logistic Regression: Sigmoid & Cost Functions
Derive the logistic (sigmoid) function and cost function for Logistic Regression.
2
K-Means Algorithm: Derivation and Convergence Proof
Derive the update steps for the K-means algorithm and prove its convergence to a local minimum.
3
K-means and Voronoi Diagrams
Explain the geometric intuition of K-means as a form of Voronoi tessellation.
4
Deriving EM for Gaussian Mixture Models
Derive the Expectation (E) and Maximization (M) steps for a Gaussian Mixture Model (GMM).
5
K-means vs. GMM: Clustering Use Cases
Compare and contrast the use cases for K-means vs. GMM for clustering tasks.
Previous module
Linear Regression and the Bias-Variance Tradeoff
Next module
Dimensionality Reduction with PCA