Neural Networks

Graduate

This course introduces the history and algorithms in neural networks, covering topics like perceptron, gradient descent learning algorithm, backpropagation, linear-separability, multi-layer perceptron, Hopefield network, self-organizing maps, restricted Boltzmann machine, self-supervised learning. Students will delve into advanced topics on deep neural network architectures like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Sort-Term Memory (LSTM), Generative Adversarial Network (GAN).