Machine learning basics. This part briefly introduces the fundamental ML problems-- regression, classification, dimensionality reduction, and clustering-- and the traditional ML models and numerical algorithms for solving the problems.
Scientific computing libraries. [slides].
Neural network basics. This part covers the multilayer perceptron, backpropagation, and deep learning libraries, with focus on Keras.
Convolutional neural networks (CNNs). This part is focused on CNNs and its application to computer vision problems.
Recurrent neural networks (RNNs). This part introduces RNNs and its applications in natural language processing (NLP).
Autoencoders. This part introduces autoencoders for dimensionality reduction and image generation.
Generative Adversarial Networks (GANs).
Deep Reinforcement Learning.
Adversarial Robustness. This part introduces how to attack neural networks using adversarial examples and how to defend from the attack.
Neural Architecture Search (NAS).