Explores neural networks' ability to learn features and make linear predictions, emphasizing the importance of data quantity for effective performance.
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.
