Grades:
Median GPA:
Mean GPA:
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
My PlannerExplore and compare past grades, professor ratings, and reviews to find the perfect class.
Grades:
Median GPA:
Mean GPA:
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Grades:
Median GPA:
Mean GPA:
5.0
Professor rating
5.0
Difficulty
1,000
Ratings given
99%
Would take again
Visit Rate My Professors
Grades:
Median GPA:
Mean GPA:
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
MATH 6343 Michael Zhang | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
MATH 6343 Michael Zhang | |||||

Grades: 249
Median GPA: B+
Mean GPA: 3.276
2
Professor rating
5
Difficulty
1
Ratings given
N/A
Would take again
Computational Biology
MATH 6343
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.

Grades: 249
Median GPA: B+
Mean GPA: 3.276
2
Professor rating
5
Difficulty
1
Ratings given
N/A
Would take again
Computational Biology
MATH 6343
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.