Spring 2026 Topic: AI for Science The goal of the course is to equip students with the tools necessary to understand and carry out research at the forefront of AI and the natural sciences. Prerequisites: Multivariable calculus, linear algebra, probability theory; familiarity with neural networks and deep learning frameworks (PyTorch or JAX); proficiency in Python. Exemplary: - Preliminaries: the AI4Science landscape, core ML concepts, automatic differentiation, Bayesian statistics, simulators, common scientific data modalities - Scientific computing infrastructure: data management, compute accelerators, benchmarking and evaluation, reproducibility - Bayesian inference: MCMC and variational methods - Generative modeling (e.g., diffusion models) and surrogate models - Differentiable programming for scientific computing - Neural network building blocks: encoding scientific inductive biases - Neural ODEs and operator learning - Uncertainty quantification - Interpretability and symbolic regression - Foundation models and LLMs for scientific applications - Case studies from across the natural sciences

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