Course Description
Network structures are increasingly common across the sciences, such as brain connectivity, gene-gene interaction, protein-protein interaction, the spread of diseases, social networks, etc. This course will introduce state-of-the-art concepts and algorithms concerning networks in statistics and machine learning. The presentation will entail a conscious balance of concepts, algorithms, and applications.
Additional Requirements for Graduate Students:
Additional and/or alternative problems of a more challenging nature will be required for graduate students on homework and exams.
Athena Title
Network Data Graphical Models
Undergraduate Prerequisite
STAT 4230/6230
Graduate Prerequisite
STAT 4230/6230 or STAT 6220 or STAT 6315 or STAT 6315E or STAT 6420 or STAT 8200 or permission of department
Semester Course Offered
Offered fall, spring and summer
Grading System
A - F (Traditional)
Student Learning Outcomes
- 1. Students who take this course will learn to produce statistical graphics to visualize complex network structures.
2. Model networks, make inferences
3. Know key libraries and modules
4. Find and use appropriate libraries and modules
5. Efficiently manipulate data types and objects
6. Write programs to implement more complex data analysis and data manipulation
7. Know the pros and cons of each language relative to each other and other programming languages
8. Know the proper use of the output from statistical software to communicate the results of statistical analyses through oral and written means.
Students will demonstrate competence through individual or group programming projects, labs, and examinations throughout the course.
- Students who take this course will learn to model networks and make inferences.
- Students who take this course will know key libraries and modules.
- Students who take this course will learn to find and use appropriate libraries and modules.
- Students who take this course will learn to efficiently manipulate data types and objects.
- Students who take this course will learn to write programs to implement more complex data analysis and data manipulation.
- Students who take this course will know the pros and cons of each language relative to each other and other programming languages.
- Students who take this course will know the proper use of the output from statistical software to communicate the results of statistical analyses through oral and written means.
- Students will demonstrate competence through individual or group programming projects, labs, and examinations throughout the course.
Topical Outline
- Graph theory
- Network mapping and visualization
- Characterization of network structure
- Network sampling
- The modeling, inference, and prediction of networks
- Network processes
- Deep learning
