Neural Networks: Multilayer PerceptronsCovers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Deep Learning FundamentalsIntroduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Multi-layer Neural NetworksCovers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Neural Quantum StatesExplores neural quantum states and their representation using artificial neural networks.
Deep Learning FundamentalsIntroduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Financial Time Series AnalysisCovers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
