DS3190

To be able to optimize a convex function with gradient descent, and how to apply these tools to optimize model parameters with respect to a cost function derived from data.

To evaluate supervised learning problems (regression and classification), by how well they generalize to new data, with cross-validation.

To express a model to fit data as a geometric object represented by a small number of parameters, with the goal of minimizing sum of squared errors, and motivated by probability assuming iid data.

To understand basic formulations, models, and algorithms for goals in linear regression, dimensionality reduction, clustering, and classification.

Upon completion of CS 3190, Students will be able to represent data points as vectors and data sets as matrices, and manipulate them with tools from linear algebra.

To be able to optimize a convex function with gradient descent, and how to apply these tools to optimize model parameters with respect to a cost function derived from data.

To evaluate supervised learning problems (regression and classification), by how well they generalize to new data, with cross-validation.

To express a model to fit data as a geometric object represented by a small number of parameters, with the goal of minimizing sum of squared errors, and motivated by probability assuming iid data.

To understand basic formulations, models, and algorithms for goals in linear regression, dimensionality reduction, clustering, and classification.

Upon completion of CS 3190, Students will be able to represent data points as vectors and data sets as matrices, and manipulate them with tools from linear algebra.