Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
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.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
