all > UGRD > INTR-D > INTR-D 280
Special Topics
Course #: INTR-D 280
Description:
Variable credit Special Topics course intended for one-time only course trials and similar offerings.
Pre Requisites:
Offered in:
2024 Fall
| Section | Class Number | Schedule/Time | Instructor | Location | |
|---|---|---|---|---|---|
| 01 | 10043 | TuTh 2:00 - 3:15 pm |
Zarringhalam,Kourosh|Moyers,Brooke T | University Hall Y01-1350 TEAL | |
|
Session:
Regular
Class Dates:
09/03/2024 - 12/13/2024
Capacity:
16
Enrolled:
0
Status:
Open
Credits:
3/3
Class Notes:
This section is administered by the College of Science and Mathematics.
Pre Requisites:
Course Attributes:
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2025 Spring
| Section | Class Number | Schedule/Time | Instructor | Location | |
|---|---|---|---|---|---|
| 01 | 13823 | TuTh 2:00 - 3:15 pm |
Zarringhalam,Kourosh|Haehn,Daniel Felix | University Hall Y01-1350 TEAL | |
|
Session:
Regular
Class Dates:
01/27/2025 - 05/14/2025
Capacity:
18
Enrolled:
0
Status:
Open
Credits:
3/3
Class Notes:
This course will introduce algorithms and data science tools for the analysis of cancer genomics, single-cell, and imaging datasets. It will also provide an overview of cancer biology, cancer research, and data disparities. Core topics include statistical hypothesis testing, supervised and unsupervised learning algorithms for cancer applications, such as identifying cancer subtypes, sparse models for biomarker discovery from gene expression and genomic mutation datasets, and graph-based models for pathway analysis in cancer. The course will cover cancer imaging technologies (e.g., H&E, IHC) and deep learning tools, including Convolutional Neural Networks, Multi-view learning, Generative Models, and Autoencoders for automated processing and annotation of cancer image data.,,Invited speakers will present research on cancer disparities, exploring both molecular and social perspectives. Each topic will be accompanied by individual or group projects. Students will also complete team-based, open-ended, research-inspired final projects. Python and/or R will be used for data analysis.,,Basic knowledge of molecular biology and programming is required. Students must have a laptop to complete assignments and run jobs on High-Performance Computing (HPC) systems.,,Student must have achieved sophomore status or must request permission from the instructor to register for this course.
Pre Requisites:
Course Attributes:
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