Warning: material has not been migrated, you may find demo fillers instead of EE 445 material.
The course material is transitioning into the Morea Framework.
See the Morea Framework Project Site for details.
EE 445 is an undergraduate level introduction to Machine Learning for Electrical and Computer Engineering students. It augments your base in probability and linear algebra (and to some extent related engineering concepts), and leverages this foundation to provide a comprehensive introduction to machine learning fundamentals.
This course is intended for undergraduates in ECE/Math, or graduate students in ICS, Economics, Math, and Business with a basic working knowledge of python, probability and linear algebra.
EE 445 is structured as a series of modules, each taking approximately 1-2 weeks to complete. Each module has material we will cover in class (usually the harder or the more important parts), and will often have supplementary material that you are encouraged to do. By design the course is open ended, where you have the option of going “ahead of lectures”, so to speak, and I am always available to help you when you do so. Each module typically has:
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.