Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
