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MATH 6343 (Overall) | |||||
MATH 6343 Pradipta Ray | |||||
MATH 6343 Michael Zhang | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
MATH 6343 (Overall) | |||||
MATH 6343 Pradipta Ray | |||||
MATH 6343 Michael Zhang | |||||
Computational Biology
School of Natural Sciences and Mathematics
Machine learning and probabilistic graphical models have become essential tools for analyzing and understanding complex systems biology data in biomedical research. This course introduces fundamental principles and methods behind the most important high throughput data analysis tools. Applications will cover molecular evolutionary models, DNA/protein motif discovery, gene prediction, high-throughput sequencing and microarray data analysis, computational modeling gene expression regulation, and biological pathway and network analysis. 3 credit hours.
Prerequisite: Some background in elementary statistics/probability or introductory bioinformatics, or instructor consent required.
Offering Frequency: Spring
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Click a checkbox to add something to compare.
Computational Biology
School of Natural Sciences and Mathematics
Machine learning and probabilistic graphical models have become essential tools for analyzing and understanding complex systems biology data in biomedical research. This course introduces fundamental principles and methods behind the most important high throughput data analysis tools. Applications will cover molecular evolutionary models, DNA/protein motif discovery, gene prediction, high-throughput sequencing and microarray data analysis, computational modeling gene expression regulation, and biological pathway and network analysis. 3 credit hours.
Prerequisite: Some background in elementary statistics/probability or introductory bioinformatics, or instructor consent required.
Offering Frequency: Spring
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Computational Biology
School of Natural Sciences and Mathematics
Machine learning and probabilistic graphical models have become essential tools for analyzing and understanding complex systems biology data in biomedical research. This course introduces fundamental principles and methods behind the most important high throughput data analysis tools. Applications will cover molecular evolutionary models, DNA/protein motif discovery, gene prediction, high-throughput sequencing and microarray data analysis, computational modeling gene expression regulation, and biological pathway and network analysis. 3 credit hours.
Prerequisite: Some background in elementary statistics/probability or introductory bioinformatics, or instructor consent required.
Offering Frequency: Spring
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
MATH 6343 (Overall) | |||||
MATH 6343 Pradipta Ray | |||||
MATH 6343 Michael Zhang | |||||
