This course is designed to be a first course in machine learning using deep learning. The course focuses on describing various building blocks that are necessary to design and train neural networks: linear layers, convolutional layers, attention layers, loss functions, data augmentation, optimization, backpropagation, … The course will discuss different types of network architectures such as classification networks, decoder, autoencoder, auto-regressive architectures for text processing, generative networks for text and images.