Postmortem Memory Analysis

Related lectures (30)

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

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

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

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

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

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

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

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

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

Explores the analysis of postmortem memory of public figures in news and social media, uncovering significant insights into memory formation.