BZAN 3307 Machine Learning and Artificial Intelligence
Machine Learning and Artificial Intelligence won't replace managers in the near term, but managers who use ML and AI well will replace those who don't. Organizations now have too much data and insufficient time for managers to consume data only in spreadsheets. Instead, the future of work involves managers designing models that, for example, segment customers, forecast sales, schedule preventative maintenance, or predict markets. This course addresses both the technical and managerial aspects of these applications. Technically, students use Python to create, evaluate, and tune multiple practical models (e.g., classifiers, trees, neural networks) in supervised and unsupervised machine learning contexts. Managerially, this course examines how organizations create value through AI applications.
Course overview
- Department
- Business Analytics
- School
- CSOM
- Credits
- 3
- Level
- Undergraduate
- Offered
- Every Fall, Every Spring
Catalog details
Requirements fulfilled
- Accounting for Finance & Consulting: Approved concentration elective options (Current University Catalog)
- General Management: Business Analytics area (Current University Catalog; choose two areas and complete each area's criteria)
- Information Systems: Information Systems elective options (Classes of 2027 and 2028; concentration ends after the Class of 2028)
- Operations Management: Operations Management elective options (Classes of 2027 and 2028; concentration ends after the Class of 2028)
- Business Analytics: Required concentration courses (Class of 2027 and beyond)
- Accounting for Finance and Consulting: Electives (6 credit hours from the following list) (Current University Catalog; students should confirm their catalog year)
Course evaluations
- Course rating
- 4.19 / 5 · Very Good
- Instructor rating
- 4.43 / 5 · Very Good
- Terms evaluated
- 10
| Term | Instructor | Course overall | Instructor overall | Well organized | Clear learning objectives | Intellectually challenging | Helpful assignments | Applicable beyond course | Attendance necessary | Weekly effort | Responses |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Do Yoon Kim | 3.6 | 3.7 | 3.9 | 4.0 | 4.6 | 4.0 | 4.2 | 4.1 | 4–6 hrs/week | 54 of 70 |
| Fall 2025 | Samuel Ransbotham | 4.4 | 4.8 | 4.8 | 4.8 | 4.8 | 4.6 | 4.7 | 4.6 | 4–6 hrs/week | 69 of 78 |
| Spring 2025 | Do Yoon Kim | 3.9 | 4.0 | 4.5 | 4.5 | 4.8 | 4.2 | 4.4 | 4.1 | 4–6 hrs/week | 74 of 86 |
| Fall 2024 | Samuel Ransbotham | 4.4 | 4.8 | 4.8 | 4.8 | 4.9 | 4.6 | 4.7 | 4.4 | 1–3 hrs/week | 76 of 96 |
| Spring 2024 | Do Yoon Kim | 3.6 | 4.0 | 4.0 | 4.0 | 4.8 | 3.8 | 4.3 | 4.3 | 4–6 hrs/week | 52 of 60 |
| Fall 2023 | Samuel Ransbotham | — | — | — | — | — | — | — | — | — | 81 of 93 |
| Spring 2023 | Pieter Vanderwerf | 4.2 | 4.0 | 4.4 | 4.4 | 4.3 | 4.5 | 4.7 | 4.5 | 1–3 hrs/week | 22 of 31 |
| Fall 2022 | Pieter Vanderwerf | 4.2 | 4.3 | 4.3 | 4.2 | 4.4 | 4.2 | 4.6 | 4.3 | 1–3 hrs/week | 27 of 30 |
| Fall 2022 | Samuel Ransbotham | 4.4 | 4.6 | 4.5 | 4.4 | 4.5 | 4.3 | 4.6 | 4.3 | 1–3 hrs/week | 54 of 62 |
| Spring 2022 | Pieter Vanderwerf | 4.3 | 4.5 | 4.6 | 4.7 | 4.5 | 4.3 | 4.7 | 4.3 | 1–3 hrs/week | 20 of 30 |
| Fall 2021 | Pieter Vanderwerf | 4.7 | 4.8 | 4.8 | 4.8 | 4.5 | 4.7 | 4.7 | 4.8 | 1–3 hrs/week | 6 of 6 |
Data freshness
Instructors
Sections
- Section 01
- Section 02
- Section 03
- Section 01
- Section 02
- Section 03