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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.
DSC 681: Applied Machine Learning
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.
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.
Sections Offered: Spring 2025 |
| Applied Machine Learning |
| 0207-681-001 |
Y. Han |
Mar 24 - May 19 |
Mon |
5:45 pm - 9:45 pm |
GC - HHE 119 |
3 |
|
| 0207-681-003 |
J. Mroz |
Mar 24 - May 19 |
Mon |
5:45 pm - 9:45 pm |
Online |
3 |
|
Sections Offered: Fall 2024 |
| Applied Machine Learning |
| 0207-681-001 |
Y. Han |
Oct 24 - Dec 19 |
Thu |
5:45 pm - 9:45 pm |
GC - HHE 209 |
3 |
|
| 0207-681-002 |
Y. Han |
Oct 21 - Dec 19 |
Mon |
5:45 pm - 9:45 pm |
GC - HHE 209 |
3 |
|