Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers controlling behavior in animals and robots through oral presentations, computational exercises, and a mini-project for hands-on neuroscientific discovery.
