2 classes matched your search criteria.
STAT 4051 is also offered in Spring 2025
STAT 4051 is also offered in Fall 2024
STAT 4051 is also offered in Spring 2024
STAT 4051 is also offered in Fall 2023
STAT 4051 is also offered in Spring 2023
STAT 4051 is also offered in Fall 2022
STAT 4051 is also offered in Spring 2022
STAT 4051 is also offered in Fall 2021
Spring 2024 | STAT 4051 Section 001: Statistical Machine Learning I (53512)
- Instructor(s)
- Class Component:
- Lecture
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person
- Enrollment Requirements:
- Stat 3301 or 3701; and Stat 4101 or 5101 or Math 5651
- Times and Locations:
Regular Academic Session
Mon,
Wed,
Fri 10:10AM - 11:00AM
UMTC, East Bank
Elliott Hall N119
- Enrollment Status:
Closed (50 of 50 seats filled)
- Also Offered:
- Course Catalog Description:
- This is the first semester of the applied statistics and statistical machine learning sequence for majors seeking a BA or BS in statistics or data science, coupled with the course STAT 4052. The course delves into the foundational statistics supporting contemporary machine learning techniques. The emphasis lies on identifying problem types, selecting appropriate analytical methods, accurate result interpretation, and hands-on exposure to real-world data analysis. The curriculum builds upon traditional multivariate statistical analysis and unsupervised learning, extending to modern machine learning topics. Topics include clustering, dimension reduction, matrix completion, factor analysis, covariance analysis, and graphical models. Additionally, advanced data structures such as text and graph data are covered. The course prioritizes the fundamental statistical principles integral to machine learning, demonstrated through the analysis and interpretation of numerous datasets. prereq: (STAT 3701 or STAT 3301) and (STAT 4101 or STAT 5101 or MATH 5651)
- Class Description:
This is the first semester of the applied statistics and statistical machine learning sequence for majors seeking a BA or BS in statistics or data science, coupled with the course STAT 4052. The course delves into the foundational statistics supporting contemporary machine learning techniques. The emphasis lies on identifying problem types, selecting appropriate analytical methods, accurate result interpretation, and hands-on exposure to real-world data analysis. The curriculum builds upon traditional multivariate statistical analysis and unsupervised learning, extending to modern machine learning topics. Topics include clustering, dimension reduction, matrix completion, factor analysis, covariance analysis, and graphical models. Additionally, advanced data structures such as text and graph data are covered. The course prioritizes the fundamental statistical principles integral to machine learning, demonstrated through the analysis and interpretation of numerous datasets.
Prerequisites: (STAT 3701 or STAT 3301) and (STAT 4101 or STAT 5101 or MATH 5651)
- Workload:
- 7-8 homework assignments, in-class midterm and final exams, one course project
- Textbooks:
- https://bookstores.umn.edu/course-lookup/53512/1243
- Instructor Supplied Information Last Updated:
- 9 November 2023
Spring 2024 | STAT 4051 Section 002: Statistical Machine Learning I (53513)
- Instructor(s)
- Class Component:
- Discussion
- Times and Locations:
Regular Academic Session
UMTC, East Bank
Mayo Bldg/Additions C231
- Auto Enrolls With:
- Section 001
- Enrollment Status:
Closed (50 of 50 seats filled)
- Course Catalog Description:
- This is the first semester of the applied statistics and statistical machine learning sequence for majors seeking a BA or BS in statistics or data science, coupled with the course STAT 4052. The course delves into the foundational statistics supporting contemporary machine learning techniques. The emphasis lies on identifying problem types, selecting appropriate analytical methods, accurate result interpretation, and hands-on exposure to real-world data analysis. The curriculum builds upon traditional multivariate statistical analysis and unsupervised learning, extending to modern machine learning topics. Topics include clustering, dimension reduction, matrix completion, factor analysis, covariance analysis, and graphical models. Additionally, advanced data structures such as text and graph data are covered. The course prioritizes the fundamental statistical principles integral to machine learning, demonstrated through the analysis and interpretation of numerous datasets. prereq: (STAT 3701 or STAT 3301) and (STAT 4101 or STAT 5101 or MATH 5651)
- Class Description:
- Student may contact the instructor or department for information.
- Textbooks:
- https://bookstores.umn.edu/course-lookup/53513/1243
ClassInfo Links - Spring 2024 Statistics Classes