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Medical Informatics Program
Education
Graduate Level Training in Medical Informatics - Sample MS Program -

The following is a sample curriculum for an MS in CS with emphasis in Biomedical Informatics, although there is some flexibility depending on a student's specific interests. Additional coursework is required for the PhD program in Computer Sciences.

 

A complete listing of graduate level courses in Computer Sciences is available on-line from the Graduate School catalog. For more information on graduate level courses in informatics, see the medical informatics courses page.

 

Computer Science 540 (Introduction to Artificial Intelligence) - 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. P: Comp Sci 367.

 

Computer Science 564 (Database Management Systems) - 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 systems. Implementation techniques for database systems. File organization, query processing, concurrency control, rollback and recovery, integrity and consistency, and view implementation. P: Comp Sci/ECE 354 & Comp Sci 367.

 

BMI 576 (Introduction to Bioinformatics) - 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. Prerequisites: Math 222 and Computer Sciences 367.

 

Computer Science 731 (Advanced Methods in Artificial Intelligence with Biomedical Applications) - Artificial Intelligence has a rich history of biomedical applications going back to the Mycin and Dendral systems. Recent advances in areas such as data mining and Baysian networks have given AI a central role in basic biomedical research and a growing role in aiding clinical decision making. This course will provide in-depth coverage of advanced topics in knowledge representation, Bayesian networks, machine learning and data mining, and planning, using biomedical applications to illustrate algorithms and approaches.

 

Computer Science 760 (Machine Learning) - 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. P: Comp Sci 540.

 

Computer Science 766 (Computer Vision) - 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. P: Comp Sci 540.

 

BMI 776 (Advanced Bioinformatics) - 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. Prerequisites:Computer Sciences 576.

 

Computer Science 787 (Advanced Algorithms and Data Structures) - 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. P: Comp Sci 577 or 509.

 

 
 
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