Supervised Learning: Linear Regression

Related lectures (30)

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Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

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

Covers supervised learning through likelihood maximization to find optimal parameters.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.