Deep and Robust Reinforcement Learning Techniques

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Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

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