Lectures related to Autocorrelation and Periodicity

Air Pollution: Correlation Analysis

Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Stylized Facts: Reproducible Research

Covers stylized facts in finance and reproducible research in scientific computing.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Parseval Relations and Fourier Transform

Covers Parseval relations, Fourier series, energy calculation, and correlation in signal processing.

Time Series: Linear Filtering and Spectral Estimation

Explores linear filtering, spectral estimation, and second-order stationarity in time series analysis.

Time Series: Fundamentals and Models

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.

Nanotoxicology: Emerging Discipline from Ultrafine Particles

Explores nanotoxicology and atmospheric chemistry, focusing on particle deposition, emission, and acid deposition.

Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Introduction to Data Analysis

Introduces data analysis basics, statistical concepts, Python libraries, and real-world applications.

Quantifying Statistical Dependence: Covariance and Correlation

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Parametric Signal Models: Matlab Practice

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Yule Walker Equations: Efficient Implementation and Correlation Analysis

Explores Yule Walker equations for efficient implementation and correlation analysis in signal processing.

Correlation: Advanced Satellite Positioning

Covers the concept of correlation in the context of advanced satellite positioning.

Causality and Invariant Machine Learning

Explores causality, correlation, and spurious correlations in machine learning, emphasizing bias mitigation and invariance across environments.

Signal Processing: Correlation and Spectral Density

Delves into correlation and spectral density of signals, explaining their significance and applications.

Dependence Concepts and Copulas

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

Properties of Time-Frequency Domain Signals

Covers the main properties of time-frequency domain signals and their limitations.

Types of Variables and Multinomial Distribution

Introduces types of variables, multinomial distribution, data characteristics, shapes of densities, correlation, and data visualization methods.