FSAN 815 - Analytics I: Statistical Learning

Credit(s): 3

ANALYTICS I:STATISTICAL LEARN
Component: Lecture
Introductory course in machine learning that covers the basic theory, algorithms, and applications with examples in financial, medicine, and engineering. How can machines learn, how they do it, and how well can they learn? Linear, nonlinear, and neural network models. Regularization methods and principles of sparsity priors to address overfitting. Training vs. testing, the VC dimension and bias-variance trade offs. Support vector machines, and deep learning networks including convolutional, recurring, generative, and transformer neural networks. Concepts reinforced in tensor flow experiments. Recommended: Basic programming skills, a first course in linear algebra and statistics.
Repeatable for Credit: N Allowed Units: 3 Multiple Term Enrollment: N Grading Basis: Student Option
Crosslisted: Crosslisted with ELEG 815.

Course Typically Offered: Fall