MSIT3103
GENERATIVE AI MODEL DEVELOPMNT
- Department
- Master of Science Info. Tech.
- Terms offered
- 2
- Data refreshed
- Jun 27, 2026
Description
In this course, students embark on a journey to harness the power of Generative AI in solving complex business problems. Through a hands-on approach, students learn best-in-class techniques to prompt, tune, and deploy various AI models, enabling them to tackle real-world challenges. Through practical projects and real-world case studies, students will apply expertise to address pressing business needs, from enhancing product design, optimizing marketing strategies, to improving productivity. By the end of the course, students emerge as adept practitioners of Generative AI, equipped to lead transformative initiatives and drive business success through innovative AI solutions.
Syllabi
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Offering history & forecast 2025–2026
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: not enough history to forecast. | 2030 Spring: not enough history to forecast. | 2030 Summer: 7% chance, medium confidence. Offered in 0 of 9 summer terms since 2018. | 2030 Intersession: 8% chance, medium confidence. Offered in 0 of 6 intersession terms since 2020. |
| 2029 | 2029 Fall: not enough history to forecast. | 2029 Spring: not enough history to forecast. | 2029 Summer: 6% chance, medium confidence. Offered in 0 of 10 summer terms since 2017. | 2029 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2028 | 2028 Fall: not enough history to forecast. | 2028 Spring: not enough history to forecast. | 2028 Summer: 5% chance, medium confidence. Offered in 0 of 11 summer terms since 2016. | 2028 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2027 | 2027 Fall: not enough history to forecast. | 2027 Spring: not enough history to forecast. | 2027 Summer: 5% chance, high confidence. Offered in 0 of 12 summer terms since 2015. | 2027 Intersession: 6% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2026 | Not offered | 1 section | Not offered | No data for this term |
| 2025 | 1 section | Not offered | Not offered | Not offered |
Future estimates combine recent offering frequency with detected multi-year cycles. Hover a cell for confidence and evidence; low-confidence estimates are marked with ~.
Meeting times
Most recent first- Thu, 6:30 PM–9:30 PM JC102 20262025
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Spring 2026 | Onur Barut | Lecture | 1 | 7 of 30 enrolled | 34831 |
| Fall 2025 | Onur Barut | Lecture | 1 | 19 of 30 enrolled | 24159 |