Linear Estimation & Prediction: Models & Methods

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Linear Estimation and Prediction

Explores linear estimation, Wiener filters, and optimal prediction in signal processing.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Parametric Signal Models: Markov Chains

Explores parametric signal models, including AR processes and Markov chains, covering synthesis, analysis, and correlation structures.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Matlab Programming: Script and Function

Explores Matlab programming with scripts and functions, vectorization, and 2D graphics.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Kalman Filter: Time Series

Covers structural modeling, state space models, and the Kalman filter in time series analysis.

Spectral & Parametric Estimation: Time Series

Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.

Matlab: 3D Surface Plotting

Covers logical arrays, 3D surface plotting, parametric curves, interpolation, and fitting in Matlab.

Neurophysiological Data Analysis

Explores neurophysiological data analysis, covering AP identification, firing rates, subthreshold activity, FFT spectral analysis, and event-triggered analysis using MATLAB.