Scientific Computing

This course will focus on the applied data science pipeline of data acquisition, data processing and integration, data modeling and analysis, and validation and delivery, commonly used in the Health industry. Topics include data normalization, scientific visualization, multivariate regression, and Artificial Neural Networks (dense, convolutional, recurrent, and adversarial). The examples and projects of this course contain 1D to 4D health data of electrocardiogram sequences, X-ray, Magnetic resonance imaging (MRI), and functional MRI images.

Graduate Level (ISC 5935)

ISC 4933/ISC 5935. Computational Probabilistic Modeling (3). Prerequisites: MAC 2312 - Calculus II, MAS 3105 – Applied Linear Algebra, and STA 4442/5440: Introduction To Probability or STA 4321/5323: Introduction to Mathematical Statistics, or the permission of the instructor. In this course, students are introduced to probabilistic programming and modeling for modern data science and machine learning applications. Algorithms for predictive inference are covered from a theoretical and practical viewpoint with an emphasis on implementation in Python. Topics include an introduction to probability and learning theory, graph-based methods, machine learning with neural networks, dimensionality reduction, and algorithms for big data. [source]

Offered Fall 2022
Schedule & Location: MWF 10:40-11:30, 499 DSL

In this course, students will explore the different elements that determine the outcome of infectious disease in humans, using COVID-19 as a case study. Starting with a very basic model that includes susceptible, infected, and recovered individuals, students will gradually incorporate factors that may affect the outcome of an outbreak, such as masking/quarantining, gain and loss of natural and vaccine-based immunity, and changing virulence/strains. After summarizing data already collected during the recent pandemic, students will inform the model to determine the conditions under which different outcomes may occur. Students will summarize their results graphically and present their findings.

No prerequisites or programming experience required. The course is designed to be accessible to all students, regardless of background or major.

Attachments:

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ISC4933_Understanding_Covid_1280x720.jpg	Advertisement	169 kB

Bioinformatics provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. Designed for biologists and biochemists seeking to improve quantitative data interpretation skills, and for mathematicians, computer scientists and other quantitative scientists seeking to learn more about computational biology. Lab exercises reinforce the classroom learning.

• Bioinformatics