Kevin J. Liu

In this course, students will survey fundamental data structures and many associated algorithms. Emphasis will be placed on matching the appropriate data structures and algorithms to application problems. Analysis of algorithms is crucial to making proper selections, so analysis is important in the course. This course assumes that students are already familiar with advanced programming techniques, including the definition of classes, and use of dynamic memory and linked data structures, including lists and trees. Even though the treatment of algorithms and data structures is mostly conceptual, students are expected to be able to transform these algorithms and data structures into programs through effective software module development.
Semester(s) taught: spring 2016, spring 2017
Course webpage (MSU D2L account required)

This course covers computational aspects of comparative genomic analysis, an important tool used to investigate cutting-edge questions in biology and human health. A primary goal of the course is to introduce quantitative foundations that underly this area and, more generally, bioinformatics and computational biology.
Semester(s) taught: fall 2014, fall 2016
Course webpage (MSU D2L account required)

This course covers advanced topics in artificial intelligence (AI) and intelligent systems. Major topics will include: (1) representation, (2) inference, (3) learning, (4) applications, and (5) other current research topics. A fundamental emphasis in the course will be dealing with uncertainty using probabilistic approaches. Course work will consist of both theory and practice.
Semester(s) taught: fall 2015, fall 2017, fall 2018
Course webpage (MSU D2L account required)