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Courses may be offered in one of the following modalities:

  • Traditional in-person courses (0–29 percent of coursework is delivered online, the majority being offered in person.)
  • Hybrid/blended courses (30–79 percent of coursework is delivered online.)
  • Online courses (100 percent of coursework is delivered online, either synchronously on a designated day and time or asynchronously as a deadline-driven course.)
  • Hyflex (Students will be assigned to attend in-person or live streamed sessions as a reduced-size cohort on a rotating basis; live sessions are also recorded, offering students the option to participate synchronously or view asynchronously as needed.)

If you are enrolled in courses delivered in traditional or hybrid modalities, you will be expected to attend face-to-face instruction as scheduled.

Applied Machine Learning (DSC-681)

Semester: Spring 2022
Number: 0207-681-001
Instructor: TBA
Days: Monday 5:45 pm - 9:45 pm
Location: Garden City - Hagedorn Hall of Enterprise 111
Credits: 3
Course Meets: March 28 - May 16

For Msba Students Only.Class Will Be Both In-Person And
Livestreamed Online For Those Students That May Prefer A
Remote Option.Class Sessions Dates Are:3/28,4/4,4/11,4/18
4/25,5/2,5/9,5/16..Prerequisite(s) 0145-602 & 0207-575

Course Materials: View Text Books
Related Syllabi: Jared Mroz for Fall 2023*
Jared Mroz for Spring 2024*

*Attention Students: Please note that the syllabi available for your view on these pages are for example only. The instructors and requirements for each course are subject to change each semester. If you enroll in a particular course, your instructor and course outline may differ from what is presented here.


Students will acquire key concepts in applied machine learning and will use software packages and applications that are relevant today such as R and Python. The course applies principles of machine learning to business problems. Topics include linear regression models, classification methods and times series forecasting.

Learning Goals:   Upon completing the course, the students will:     1. Describe the scope of Machine Learning and is importance in a Business Environment2. Analyze real world scenarios and select the appropriate Machine Learning techniques to utilize.3. Develop Machine Learning models that can support an Organizations decision process 4. Generate solutions using R and/or Python popular packages 5. Examine the results obtained using different Machine Learning Techniques6. Explain the difference between supervised and unsupervised machine learning7. Illustrate stationary, trend and, seasonal time series patterns and their application in Forecasting

*The learning goals displayed here are those for one section of this course as offered in a recent semester, and are provided for the purpose of information only. The exact learning goals for each course section in a specific semester will be stated on the syllabus distributed at the start of the semester, and may differ in wording and emphasis from those shown here.


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