Clustering & Density Estimation

Related lectures (55)

Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Covers the Boltzmann Machine, a type of stochastic recurrent neural network.

Explores data visualization techniques, design impact, and interactive applications for effective information communication.

Covers statistical analysis techniques, hypothesis testing, and distribution models using R software.

Explores centroid, medoid, homogeneity, separability in clustering, quality evaluation, stability, expert knowledge, and clustering algorithms.

Explores challenges and solutions in analyzing big multi-dimensional data, focusing on complex data types and anomaly detection.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Explores equilibrium and stability of a vertical metronome system with springs.

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