Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
