Data Handling: Problems and Distributions

Related lectures (29)

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Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Explores the curse of dimensionality, variable selection methods, coefficient of determination, and limitations of filtering techniques.

Covers the basics of data analysis, focusing on statistical concepts and correlation measures.

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Covers data wrangling techniques using Apache Hive for efficient big data management.

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

Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.