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Feature Selection
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Related lectures (32)
Describing Data: Statistics and Hypothesis Testing
Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.
Dependence Concepts and Copulas
Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.
Causality and Invariant Machine Learning
Explores causality, correlation, and spurious correlations in machine learning, emphasizing bias mitigation and invariance across environments.
Complex Systems: Epidemics
Delves into complex systems, strange attractors, and epidemics, emphasizing the influence of psychological factors and the role of contagion.
Quantifying Statistical Dependence: Covariance and Correlation
Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Copulas: Properties and Applications
Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.
Dependence and Correlation
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Multi-dimensional correlation spectroscopy: INEPT and HSQC
Explains coherence transfer techniques in multi-dimensional NMR spectroscopy.
Stylized Facts: Reproducible Research
Covers stylized facts in finance and reproducible research in scientific computing.
Mutual Information in Biological Data
Explores mutual information in biological data, emphasizing its role in quantifying statistical dependence and analyzing protein sequences.
Air Pollution: Correlation Analysis
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Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
PCA: Key Concepts
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Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
How to Lie with Statistics
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Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.
Estimation and Correlation
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Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.
Signal Processing Fundamentals
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Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Rolling Bearings: Materials and Types
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Explores the constitution and materials of rolling bearings, types, and selection criteria based on loads and speeds.
Dimensionality Reduction: Curse of Dimensionality
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Explores the curse of dimensionality, variable selection methods, coefficient of determination, and limitations of filtering techniques.
PCA: Key Concepts
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Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Parseval Relations and Fourier Transform
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Covers Parseval relations, Fourier series, energy calculation, and correlation in signal processing.
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