Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.
