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.
Deep Learning FundamentalsIntroduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Deep Learning FundamentalsIntroduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Reinforcement Learning ConceptsCovers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Deep Neural NetworksCovers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Financial Time Series AnalysisCovers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Neural Networks OptimizationExplores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.