Non-smoothness and Compressive Sensing

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Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

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

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

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

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