EE 645 is an graduate level course in machine learning, and a foundational course for graduate students who would like to use AI/machine learning in their research.
Current students: please sign on to the class discord server here.
The Spring 2024 edition welcomes students from multiple departments: in addition to the traditional ECE, Computer Science, and other STEM departments (ME, CEE, Math, Astronomy), we have students from social sciences, business and urban planning. Basic familiarity with statistics, probability and some linear algebra, supplemented with a willingness to learn fundamentals, is all that is required. Ideally, students should have had some exposure to using machine learning/AI (even if it is only YouTube videos or playing with them as black boxes). The goal is for all students to pick up the central tenets of this field in a way that can guide them to use AI/machine learning in a sophisticated and nuanced fashion.
EE 645 is structured as a series of modules, each taking approximately 2-3 weeks to complete. Given the diversity of students, we will tailor courses towards multiple categories of students (theory, simulations, developing applications for non-traditional areas). The course will operate in partially flipped mode, where we will delegate some topics to recordings and use class time for threading multiple perspectives together.
Modules have:
Narayana Prasad Santhanam is a Professor of Electrical and Computer Engineering at the University of Hawaii. My research interests are at the intersection of machine learning, information theory and statistics. A particular focus is on high dimensional and complex problems, that are not amenable to traditional statistical methods and guarantees.