MATH 463: Mathematics of Machine Learning and Industrial Applications II
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Course Information from the University Catalog
Credits: 2
Basic mathematical and probabilistic models and derivations for convolutions, stability, regularization, inverse and optimal control problems, and dynamical systems in the context of semi-supervised learning. Mathematical and numerical aspects of stochastic descent methods, Nesterov accelerated gradient, AdaGrad, Adam, with applications to convolutional, deep, and ODE networks. Further applications include imaging and computer vision, saliency maps, segmentation, satellite Imagery, and physics informed learning. Offered by Mathematics. Limited to three attempts.
Registration Restrictions:
Required Prerequisites: (MATH 462*C or 462*XS).
* May be taken concurrently.
C Requires minimum grade of C.
XS Requires minimum grade of XS.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
This course is graded on the Undergraduate Regular scale.