Social Capital in Online Networks

Related lectures (29)

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

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

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

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.

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

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

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

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.