Introduction and artificial neural networks: their history, the perceptron and its limitations, boolean and real multi-layered perceptron, topologies, universal function approximation, decision boundaries, activations, sufficiency of architecture, Training I: recall on Gradient, Jacobian, Hessian; what is learning, empirical risk minimization, gradient descent, calculus of backpropagation, Training II: convergence issues, loss surfaces, momentum, optimization, second order methods, regularization strategies, initialization, Convnets I: definitions, types of convolutions, pooling, prominent architectures, Convnets II: vision models with convnets, feature pyramid, transposed convolution, object detection and segmentation, Sequence modeling I: Time series, RNNs, Sequence modeling II: Memory, LSTMs, sequence prediction, Attention: transformers, sequence to sequence predictions, LLMs and their downstream applications, Representation learning: autoencoders, self-supervision, unsupervised approaches, contrastive learning, Generative DL: Variational autoencoders, GANs and diffusion, Deep reinforcement learning: deep q-learning, Graph Neural networks, AI ethics