ME 405 Machine Learning for Mechanical Engineering
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Statistics in Python, data management, visualization, regression and classification models, feature engineering, usage of databases, supervised and unsupervised learning, reinforcement learning, active learning, Bayesian optimization, Gaussian process, decision trees and forests, neural networks, convolutional neural networks for mechanical engineering applications (e.g. microscopy image analysis, material structure-property relationship), atomistic descriptors, machine learning potentials for molecular dynamics, self-driving lab.
Credit units: 3 ECTS Credit units: 5, Prerequisite:
(MATH 220 or MATH 224 or MATH 225 or MATH 241) and (MATH 230 or MATH 255 or MATH 260) and (CS 102 or CS 114 or CS 115) and (CHEM 201 or PHYS 212).
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