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ICS 352 - Machine Learning
Fall 2021, Section 01

search actionsID #Subj#SecTitleDatesDaysTimeCrdsStatusInstructorDelivery MethodLoc
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000455 ICS 352 01 Machine Learning
08/26 - 12/09
Th
6:00pm - 9:20pm
4.0 Full Sulieman, Dalia
Remote Teaching No In-Person Location: z MnSCU Metropolitan State University


Meeting Details
DatesDaysTimeBuilding/RoomInstructor
8/26/2021 - 12/9/2021 Th 6:00pm - 9:20pm n/a Sulieman, Dalia

Notes
  • Note: This is a completely online course, not an independent study. Course has required synchronous online meetings that require intermediate computer/Internet skills, webcam and microphone and may require remotely proctored exams. For online learning and course access information go to www.metrostate.edu/solr.
  • Note: Students are responsible to both be aware of and abide by prerequisites for ICS courses for which they enroll, and will be administratively dropped from a course if they have not met prerequisites.
  • First day attendance is mandatory.
  • Synchronous Completely Online; required meetings.
  • Note: Information explaining the differences between course types and the in-person instruction plans can be found at: https://www.metrostate.edu/deliverymethods
  • Converted Due to COVID-19

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

Seat Availability
Status: Full Size: 32 Enrolled: 32 Seats Remaining: 0

Prerequisites (Courses and Tests)
This course requires the following prerequisite
ICS 240 - Introduction to Data Structures

Restrictions
  • Requires minimum credits: 30

Add/Drop/Withdraw
Full refund is available until August 27, 2021, 11:59PM CST.
The last day to add this course is August 27, 2021. The last day to drop this course is August 27, 2021.
The last day to withdraw from this course is November 22, 2021.

Tuition and Fees (Approximate)

Tuition and fee information is not yet available for this semester.

Course Level
Undergraduate

Description
This course presents the key algorithms and theory of machine learning. Students will examine supervised and unsupervised learning algorithms. And they will gain an understanding of machine learning foundational concepts used in artificial intelligence, statistics and data science. Topics include learning algorithms used in recent application as autonomous vehicles, google search, and Facebook photo tags.

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