TASI Lectures on Physics for Machine Learning
<|reference_start|>TASI Lectures on Physics for Machine Learning: These notes are based on lectures I gave at TASI 2024 on Physics for Machine Learning. The focus is on neural network theory, organized according to network expressivity, statistics, and dynamics. I present classic results such as the universal approximation theorem and neural network / Gaussian process correspondence, and also more recent results such as the neural tangent kernel, feature learning with the maximal update parameterization, and Kolmogorov-Arnold networks. The exposition on neural network theory emphasizes a field theoretic perspective familiar to theoretical physicists. I elaborate on connections between the two, including a neural network approach to field theory.<|reference_end|>
@article{halverson2024tasi,
    title={TASI Lectures on Physics for Machine Learning},
    author={Jim Halverson},
    journal={arXiv preprint arXiv:2408.00082},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00082},
    primaryClass={hep-th cs.LG hep-ph}
}