Jiang Bian, PhD, MS
Division of Biomedical Informatics
University of Arkansas for Medical Sciences
4301 W. Markham St. Slot 633-1, Little Rock, AR 72205 Email: jbian [at] uams.edu or jxbian [at] ualr.edu
Work: (501) 603-1779
CPSC 7373 is a graduate-level introductory course into the field of Artificial Intelligence (AI) offered in the Department of Computer Science at UALR. AI is also one of the core courses in the Bioinformatics program offered jointly by UALR and UAMS. We will start the course with introductions to some basic elements of AI, such as knowledge representation, interference, machine learning, neural networks, graph theory based network analysis, natural language processing, information retrieval, problem solving, and learning methods in general. And we will quickly dive into various specific research topics using these basic AI elements, such as social network analysis, and graph theoretical analysis of human brain connectome.
Moreover, we are now in the era of the "big data" revolution where nearly every aspect of computing engineering is being driven by large-data processing and analysis, often in real or near-real time. It is important for the students to gain exposure to big data analytic problems and applications. Especially, this class aims to give the students insight to the basics of cloud computing, and hands-on experiences with the state-of-the-art programming paradigm--MapReduce--for a cloud computing environment to address the computational requirements of the big data problem.
The design of the class does have a strong focus on bioinformatics. The applications and problems presented in the class are derived from the instructor's research in bioinformatics using AI elements, such as the study of functional human brain networks and mining social media content for public health issues.
The preliminary list of topics that will be covered in this class include, but are not limited to:
The final list of topics is subjected to change based on the survey conducted at the beginning of the class, according to the students' background and interests.