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Searched for: 1 subject found.
2.168 Learning Machines
(New)(
)
Prereq: None
Units: 3-0-9
Lecture: MW11-12.30 (37-212)
Introduces fundamental concepts and encourages open-ended exploration of the increasingly topical intersection between artificial intelligence and the physical sciences. Energy and information with their respective optimality conditions, as well as ordinary and partial differential equations, are used to define supervised and unsupervised learning algorithms. Subsequently, physical systems with complex constitutive relationships are drawn from elasticity, biophysics, fluid mechanics, hydrodynamics, acoustics, and electromagnetics to illustrate how machine learning-inspired optimization can approximate solutions to forward and inverse problems in these domains. Students should have familiarity with calculus, probability theory and programming; however, necessary concepts will be re-introduced and reinforced as necessary throughout the class.
G. Barbastathis
No textbook information available
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