MATH0218A-S21
Statistical Learning
Statistical Learning
This course is an introduction to modern statistical, machine learning, and computational methods to analyze large and complex data sets that arise in a variety of fields, from biology to economics to astrophysics. The theoretical underpinnings of the most important modeling and predictive methods will be covered, including regression, classification, clustering, resampling, and tree-based methods. Student work will involve implementation of these concepts using open-source computational tools. (MATH 0216) 3 hrs. lect./disc.
This course is an introduction to modern statistical, machine learning, and computational methods to analyze large and complex data sets that arise in a variety of fields, from biology to economics to astrophysics. The theoretical underpinnings of the most important modeling and predictive methods will be covered, including regression, classification, clustering, resampling, and tree-based methods. Student work will involve implementation of these concepts using open-source computational tools. (MATH 0216) 3 hrs. lect./disc.
- Term:
- Spring 2021
- Location:
- McCardell Bicentennial Hall 216(MBH 216)
- Schedule:
- 10:20am-11:10am on Monday, Wednesday, Friday (Feb 24, 2021 to May 21, 2021)
- Type:
- Lecture
- Course Modality:
- In-Person
- Instructors:
- Kara Karpman
- Subject:
- Mathematics
- Department:
- Mathematics
- Division:
- Natural Sciences
- Requirements Fulfilled:
- DED
- Levels:
- Undergraduate
- Availability:
- View availability, prerequisites, and other requirements.
- Course Reference Number (CRN):
- 22117
- Subject Code:
- MATH
- Course Number:
- 0218
- Section Identifier:
- A