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DATA 401 - Statistical Machine Learning
Spring 2021, Section 50

search actionsID #Subj#SecTitleDatesDaysTimeCrdsStatusInstructorDelivery MethodLoc
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000609 DATA 401 50 Statistical Machine Learning
01/11 - 05/03
n/a
n/a
4.0 Open Jacobson, David
Completely Online-Asynchronous Location: z MnSCU Metropolitan State University


Meeting Details
DatesDaysTimeBuilding/RoomInstructor
1/11/2021 - 5/3/2021 n/a n/a n/a Jacobson, David

Notes
  • Note: Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at MATH@metrostate.edu. Note: This is a completely online course, not an independent study. Course has no required in-person or synchronous meetings. There are required online activities and assignments each week. May require remotely proctored exams that require a webcam and microphone. Intermediate computer/Internet skills required. For online learning and course access information go to www.metrostate.edu/solr.

Location Details
Offered through: Metropolitan State University.
Campus: Metropolitan State University. Location: z MnSCU Metropolitan State University.

Seat Availability
Status: Open Size: 24 Enrolled: 20 Seats Remaining: 4

Prerequisites (Courses and Tests)
[STAT 311 - Regression Analysis AND ICS 352 - Machine Learning AND DATA 211 - Data Science and Visualization]
Restrictions
  • Requires minimum credits: 30

Add/Drop/Withdraw
Full refund is available until January 15, 2021, 11:59PM CST.
Adding course is closed. Dropping course is closed.
The last day to withdraw from this course is April 12, 2021.

Tuition and Fees (Approximate)

Tuition and Fees (approximate):

Tuition -resident: $1,270.36
Tuition -nonresident: $1,270.36
Approximate Course Fees: $148.44

Course Level
Undergraduate

Description
Statistical machine learning (often referred to simply as statistical learning) has arisen as a recent subfield of statistics. It emphasizes the interpretability, precision, and uncertainty of machine learning models. This course assesses the accuracy of several supervised and unsupervised machine learning models for both regression and classification. Topics include the bias-variance trade-off, training and test datasets, resampling methods, shrinkage and dimension reduction methods, non-linear modeling techniques such as regression splines and generalized additive models, and decision tree-based methods. Applications include examples from medicine, biology, marketing, finance, insurance, and sports.

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