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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. 

Learn more here

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/tutorial studying and revision ● 42 h: studying for and taking weekly online tests ● 40 h: examination preparation ● 41 h: preparation and presentation of weekly exercises
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/Project Completion
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.

Dhara Atul Mungra, M.Sc.

Researcher and Lecturer
Inaugural Gustav Struve Fellow in Global Responsible AI
Universität Mannheim
Fakultät für Sozial­wissenschaften
Social Data Science
Büro/Office: Institut für Mittelstandsforschung
Schloss – Raum EO 269
68161 Mannheim