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Related lectures (30)

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Spectral Estimation Methods Log in to Mediaspace to watch this video

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression Log in to Mediaspace to watch this video

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Linear Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Frequency Estimation (Theory)Log in to Mediaspace to watch this video

Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.

Understanding Data Attributes Log in to Mediaspace to watch this video

Covers the analysis of various data attributes and linear regression models.

Signals, Instruments, and Systems Log in to Mediaspace to watch this video

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Cross-Validation: Techniques and Applications Log in to Mediaspace to watch this video

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

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