Model EvaluationExplores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Financial Time Series AnalysisCovers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Machine Learning BasicsIntroduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.