Applied Deep Learning and Ai

This course provides a non-mathematical introduction to the principles and architectures of deep learning and generative AI models. Designed for undergraduates in the Applied Data Science minor, the curriculum covers the mathematical foundations of neural networks and their application to spatial, temporal, and multimodal data. Students will examine the mechanics of convolutional and recurrent architectures, the self-attention mechanism in Transformers, and the training objectives of Large Language Models (LLMs). The course also addresses optimization strategies, reinforcement learning for model alignment, and generative paradigms, including diffusion and autoregressive models. Emphasis is placed on understanding model internal representations, architectural tradeoffs, and the evaluation of complex AI systems.