Machine Learning and Data Mining

Dr. Caragea instructs students in her Bioinformatics Group Dr. Caragea instructs students in her Bioinformatics Group

Overview

Machine learning and data mining research in the department aims to design algorithms and to develop tools for analyzing large amounts of data. The Bioinformatics group focuses on molecular sequence and gene expression data, while the KDD group focuses on applications in artificial intelligence and software engineering.

Related Courses

  • CIS 530: Introduction to Artificial Intelligence
  • CIS 590: Top/Introduction to Genomics and Bioinformatics
  • CIS 730: Artificial Intelligence
  • CIS 732: Machine Learning and Pattern Recognition
  • CIS 734: Introduction to Genomics an Bioinformatics
  • CIS 761: Data Base Management Systems

Faculty

dcaragea's picture
Associate Professor
E2166
(785) 532-7908
bhsu's picture
Associate Professor
E2164
(785) 236 8247

Research Groups and Laboratories

Machine Learning and Bioinformatics

The MLB Group aims to design algorithms and to develop tools for analyzing large amounts of data, in particular, molecular sequence and gene expression data. Our main projects are focused on:

  • Ontology engineering and classifier learning from semantically heterogeneous data sources
  • Genomic sequence analysis, alternative splicing discovery and gene prediction
  • Gene regulatory network discovery from gene expression data and sequence information

 

Laboratory for Knowledge Discovery in Databases

The Laboratory for Knowledge Discovery in Databases (KDD) is a research group in the Computer Science (CS) Department at Kansas State University. Its research emphasis is in the areas of applied artificial intelligence (AI) and knowledge-based software engineering (KBSE) for decision support systems. More specifically, we are interested in machine learning, data mining and knowledge discovery from large spatial and temporal databases, human-computer intelligent interaction (HCII), and high-performance computation in learning and optimization. In our research, we look for ways to systematically decompose analytical learning problems based upon information theoretic and probabilistic criteria, so that the most appropriate machine learning methods may be applied to the resulting transformed problems.

Research Partners

Corporate

Academic

Kansas State University

Related Projects