Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
