This short course will provide an overview of the statistical underpinnings of Deep Learning (DL) and Artificial Intelligence (AI). The course will trace the evolution of AI models, beginning with Dense Neural Networks before progressing through Convolutional (CNN) and Recurrent (RNN) frameworks to modern Transformers, Diffusion models, and AI agents. Beyond model architecture, we will also explore the relationship between AI and statistics: how AI can advance statistical analyses and research, and conversely how statistics can advance AI.

Students are required to have knowledge of all material covered in SISBID Module 3: Supervised Methods for Statistical Learning. Knowledge of material covered in SISBID Module 4: Unsupervised Methods for Statistical Learning is recommended, but optional.