MATH-355 Mathematics of Machine Learning

An introduction to machine learning with a focus on the mathematics required to perform various algorithms. Topics include linear mappings, inner product spaces, orthogonality, matrix decompositions, gradients, supervised and unsupervised machine learning, principal components analysis, and artificial neural networks. Familiarity with rudimentary computer programming recommended.

Maximum Enrollment

Standard Course (40)

(Quantitative and Symbolic Reasoning.)

Credits

1

Prerequisite

Math 216 and Math 224

Offered

Fall