Supervised Learning: Likelihood Maximization

Related lectures (30)

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

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

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

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

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

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.