Undergraduate/Graduate course, University of Nevada, Reno, Computer Science and Engineering, 2019
This course is a combined graduate/undergraduate level introduction to artifical intelligence course. Problem solving, search, and game trees. Knowledge representation, inference, and rule-based systems. Semantic networks, frames, and planning. Introduction to machine learning, neural-nets, and genetic algorithms. (Taught Fall 2019, Approx. 65 students).
Graduate course, University of Nevada, Reno, Computer Science and Engineering, 2019
This course aims to introduce students to practical tools used to solve various types of machine learning problems. This course focuses on both standard machine learning techniques and deep learning methods. The applications being explored are data imputation, natural language processing, object recognition, and trajectory optimization. Students will work on a semester project in which they must apply some of the tools to a problem area of their choosing, with the expectation of a resulting conference paper. These projects will illustrate that students are able to use these methods to effectively solve modern problems. (Taught Spring 2019, Co-Instructor: Dr. David Feil-Seifer, Approx. 21 students).