The following are courses taught by Informatics faculty and/or related to Medical and/or Bioinformatics. By no means is this a comprehensive list. Course titles link to most recent syllabi or homepages if available.

Courses in our Department

BMI 576 - Introduction to Bioinformatics. 3 credits. The goals of this course are to provide an understanding of the fundamental computational problems in molecular biology and a core set of widely used algorithms. This is the first of two courses on bioinformatics. The topics it will cover include: pairwise sequence alignment, multiple sequence alignment, finding genes in DNA sequences, phylogenetic tree construction, and genome mapping and sequencing. This is currently being taught as a special topics course in Computer Sciences. Prerequisites: Math 222 and Computer Sciences 367.

BMI 776 - Advanced Bioinformatics. 3 credits. The goals of this course are to provide an understanding of the fundamental computationalproblems in molecular biology and a core set of widely used algorithms. This is the second of two courses on bioinformatics. The topics it will cover include: probabilistic methods for sequence modeling, gene expression analysis, phylogenetic tree construction, protein structure prediction, RNA modeling, whole-genome analysis, and algorithms for exploiting biomedical text sources. A precursor to this course was taught as a special topics course in Computer Sciences in the Fall 1999 semester. Prerequisites: Computer Sciences 576.

Courses in Computer Sciences

Comp Sci 540- Introduction to Artifical Intelligence. 3 credits. Principles of knowledge-based search techniques; automatic deduction; knowledge representation using predicate logic, semantic networks, connectionist networks, frames, rules. Applications in problem solving, expert systems, game playing, vision, natural language understanding, learning robotics, Lisp programming. Prerequisites: Comp Sci 367.

Comp Sci 564- Database Management Systems: Design and Implementation. 3-4 credits. What a database management system is; different data models currently used to structure the logical view of the database: relational, hierarchical, and network. Hands-on experience with relational and network-based database sytems. Implementation techniques for database systems. File organization, query processing, concurrency control, rollback and recovery, integrity and consistency, and view implementation. Prerequisites: Comp Sci/ECE 354 & Comp Sci 367.

Comp Sci 731- Advanced Artifical Intelligence. 3 credits. Novel techniques within BayesianNetworks, Machine Learning and Data Mining, Planning and Computer Vision have proven useful for many real-world problems. This course will cover some of the most important recent algorithms from these areas and will illustrate their use with biomedical applications. Prerequisites: Computer Sciences 540.

Comp Sci 760- Machine Learning. 3 credits. Computational approaches to learning: including inductive inference, explanation-based learning, analogical learning, connectionism, and formal models. What it means to learn. Algorithms for learning. Comparison and evaluation of learning algorithms. Cognitive modeling and relevant psychological results. Prerequisites: Comp Sci 540.

Comp Sci 766- Computer Vision. 3 credits. Fundamentals of image analysis and computer vision; image acquisition and geometry; image enhancement; recovery of physical scene characteristics; shape-from techniques; segmentation and perceptual organization; representation and description of two-dimensional objects; shape analysis; texture analysis; goal-directed and model-based systems; parallel algorithms and special-purpose architectures. Prerequisites: Comp Sci 540.

Comp Sci 787- Advanced Algorithms and Data Structures. 3 credits. Algorithms for graph manipulation, geometry, matrix multiplication, string processing, information retrieval, etc. Mathematical models and analyses. Lower bounds. Probabilistic, distributed, and parallel algorithms. Advanced data structures. Prerequisites: Comp Sci 577 or 509.

Comp Sci 838- Topics in Computing. 3 credits. From advanced areas. Contents may vary. May be repeated any number of times for credit. Recent topic: Machine Learning for Text Analysis. Prerequisite: instructor's consent.

Courses in Other Departments

Ind Eng 617- Health Information Systems. 3 credits. Provides grounding in core concepts of health information systems. Major applications include clinical information systems, language and standards, decision support, image technology and digital libraries. Evaluation of IE tools and perspectives designed to improve the quality, efficiency and effectiveness of health information. Prerequisites: Senior or Graduate standing, or instructor's consent.