Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
