Reinforcement Learning ConceptsCovers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Machine Learning FundamentalsCovers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Machine Learning BasicsIntroduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.