AMS 691 Topics in Applied Mathematics
Varying topics selected from the list below if sufficient interest is shown. Several topics may be taught concurrently in different sections: Advanced Operational Methods in Applied Mathematics, Approximate Methods in Boundary Value Problems in Applied Mathematics, Control Theory and Optimization Foundations of Passive Systems Theory, Game Theory, Mixed Boundary Value Problems in Elasticity, Partial Differential Equations, Quantitative Genetics, Stochastic Modeling, Topics in Quantitative Finance.
AMS 691.01: Medical Image Analysis
This course explores the fundamental principles and algorithms used in medical image processing and analysis. Key topics include interpolation, registration, enhancement, feature extraction, classification, segmentation, quantification, shape analysis, motion estimation, and visualization, including traditional and machine learning techniques. Both anatomical and functional image analysis will be covered, using data from common medical imaging modalities. Through projects and assignments, students will gain hands-on experience working with real medical imaging data. No prerequisites.
0-3 credits; ABCF grading
No course materials
Topics:
Interpolation, registration, enhancement, feature extraction, classification, segmentation, quantification, shape analysis, motion estimation, and visualization
Learning Outcomes:
Explores the fundamental principles and algorithms used in medical image processing and analysis
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AMS 691.02: Large Language Models
No course materials
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AMS 691.03: Data Management
This course teaches how to manage databases, which are important tools for organizations. Students will learn about the different parts of a database system, how data is stored, and how to get information from databases. The course will also cover how to design and build large databases, including creating logical structures, handling multiple processes at the same time, distributing data, and managing databases.
There are no prerequisites for this course, and we will start learning R and SQL from the very beginning. However, having some prior experience would be helpful.
3 credits; ABCF grading
Course materials will be supplied by the instructor via Brightspace
Topics:
Students will also explore topics like data warehousing (storing large amounts of
data), cleaning data, and data mining (finding useful information from data). The
course includes hands-on practice with R and SQL, where students will clean, organize,
and combine raw data to prepare it for analysis. This practical experience will help
students develop key skills in getting data ready for use in projects.
Learning Outcomes:
By the end of this course, students will understand
--the basics of managing databases, which are important tools for organizations;
--receive practical experience in designing and building databases, cleaning up raw
data, and preparing it for analysis using R and SQL.