Genetics: Project R #2

Related lectures (30)

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

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

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

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

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

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

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

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