This course covers the theory and foundations of Artificial Neural Networks (ANN) and various ANN architectures, such as the single and multi- layer perceptrons, Hopfield and Kohonen networks, and deep learning architectures (convolutional neural networks, autoencoders, restricted Boltzman machines, recurrent networks and LSTMs, and generative adversarial networks). Students will be expected to develop systems for machine learning problems from the computer vision and natural language understanding areas.