DS 601 AI product development
Goals and learning outcomes
Aims of module
The primary aim of this course is to equip students with the skills to transform their project ideas into viable business products, all while maintaining a steadfast focus on the core principles of responsible computing and explainable AI. Students will collaborate with organizations in technology, policy, and government, developing projects that advance key areas like journalism, fact-checking, digital literacy, and healthcare. The program provides continuous mentoring and access to vital resources and networks, enabling participants to move beyond traditional
Prerequisites
To excel in this course, students should have a solid foundation in data science, data analysis, and machine learning algorithms. A basic understanding of Large Language Models (LLMs) is essential. Working knowledge of advanced AI concepts like Retrieval-Augmented Generation (RAG), various prompting methods, and practical experience in developing prototypes with AI tools will be highly beneficial.
Application
Please note that you have to formally apply via webform to participate in this course.
Course Information
| Form of module | Lecture and Tutorial |
|---|---|
| Type of module | Foundations of AI product development |
| Level | Masters |
| ECTS | 9 (270 hours), Hours per semester present: 56 h (4 SWS) |
| Workload |
Self-study: 152 h per semester ● 91 h: pre and post lecture/ |
| Media | Lecture slides and exercises will be available online. |
| Literature | Since the course emphasizes practical applications and AI product development, project-specific literature will be provided during the sessions alongside the presentation slides. |
| Methods | Lecture elements, weekly product development updates |
| Form of assessment |
Product/ |
| Admission requirements for assessment | Oral participation, contributions towards the project, presentations, compulsory attendance |
| Duration of assessment | 90 mins |
| Language | English |
| Offering | Spring semester |
| Lecturer | Dhara Mungra |
| Person in charge | Prof. Dr. Marc Ratkovic |
| Duration of module | 1 semester |
| Semester | Second or fourth semester |
| Range of application | MMSDS, Data Science, Business School, CDSS |

Dhara Atul Mungra, M.Sc.
Fakultät für Sozialwissenschaften
Social Data Science
Büro/Office: Institut für Mittelstandsforschung
Schloss – Raum EO 269
68161 Mannheim