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Lecture
Parametric Signal Models: Markov Chains
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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.
Spectral Estimation Methods
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Linear Estimation & Prediction: Models & Methods
Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.
Signal Models and Methods: Parametric vs Nonparametric
Provides an overview of signal models and methods in statistical signal processing.
Signal Processing Fundamentals
Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Modeling Neurobiological Signals: Markov Chains
Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.
Statistical Signal Processing Tools
Explores statistical signal processing tools for wireless communications, including spectral estimation and signal detection, classification, and adaptive filtering.
Gaussian Mixture Regression: Modeling and Prediction
Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.
Statistical Signal Processing
Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
