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Related lectures (18)

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Model Selection in Statistics

Explores model selection in statistics, discussing principles, probabilistic models, characteristics evaluation, and data visualization methods.

Data Exploration: Normal Distribution

Explores data distribution strategies, normal modeling, and checking normality with graphical tools.

Graphical Concepts in Matlab/Octave

Explores plotting 2D graphs, handling errors, creating bar charts, and visualizing vector fields in Matlab/Octave.

Deep Learning: Data Representations and Neural Networks

Explores data representations, histograms, neural networks, and deep learning concepts.

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Families of Distributions: PDFs and CDFs

Explores PDFs and CDFs for discrete and continuous random variables, including Gaussian, uniform, and exponential distributions.

Kernel Density Estimation: Bandwidth Selection and Curse of Dimensionality

Covers Kernel Density Estimation focusing on bandwidth selection, curse of dimensionality, bias-variance tradeoff, and parametric vs nonparametric models.

Non-Parametric Estimation: Kernel Density Estimators

Explores non-parametric estimation using kernel density estimators to estimate distribution functions and parameters, emphasizing bandwidth selection for optimal accuracy.