|CISC371, Nonlinear Optimization: Fall 2019|
When we use machine learning or data analytics methods on structured numerical data, we often want to find an optimal answer. For example, if we want to model the data as being linearly related, we might want to minimize the error between the model and the data. In this course, we will explore data analysis where we have a single number that indicates how well the model represents the data. This number is the objective that we want to optimize. Some of the models that we will study include: neural networks; linear relationships within the data where we have a nonlinear objective, such as ridge regression and the ``lasso''; and dividing data into two classes based on how the data is labeled, such as the support vector machine or SVM.
The course contents, including the schedule, notes, and supplementary material, are provided in the onQ learning management system.