Course Search
3 credits
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. 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.DSC 681: Applied Machine Learning
Learning Goals
Sections Offered: Fall 2026
Course No.
Day
Credits
Expand details
0207-681-001
Mon
3
0207-681-002
Thu
3
0207-681-801
Thu
3
Sections Offered: Summer 2026, Summer II
Course No.
Day
Credits
Expand details
0207-681-001
Mon, Wed
3
Sections Offered: Spring 2026
Course No.
Day
Credits
Expand details
0207-681-001
Mon
3