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MATH 275A: Topics in Applied Math I

This course will discuss several algorithmic topics in two areas: quantum physics and machine learning. In quantum physics, topics include Hartree-Fock, density function theory, quantum Monte Carlo. In machine learning, we will discuss several problems in optimization, implicit regularization, generalization, generative modeling, and RL. Prerequisite: basic quantum mechanics and machine learning. May be repeated for credit. NOTE: Undergraduates require instructor permission to enroll. Undergraduates interested in taking the course should contact the instructor for permission, providing information about relevant background such as performance in prior coursework, reading, etc.
Terms: Aut | Units: 3 | Repeatable for credit
Instructors: Ying, L. (PI)
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