AI in Practice: Implementing Real-World Solutions (Wintersemester 2023/2024)
Dozent:
Prof. Dr. Gerard de Melo
(Artificial Intelligence and Intelligent Systems)
,
Michael Mansfeld
Website zum Kurs:
https://hpi.de/entrepreneurship/ai-in-practice.html
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.10.2023 - 31.10.2023
- Prüfungszeitpunkt §9 (4) BAMA-O: 22.01.2024
- Lehrform: Projektseminar
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
Studiengänge, Modulgruppen & Module
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-K Konzepte und Methoden
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-S Spezialisierung
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-T Techniken und Werkzeuge
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- DANA: Data Analytics
- HPI-DANA-K Konzepte und Methoden
- DANA: Data Analytics
- HPI-DANA-T Techniken und Werkzeuge
- DANA: Data Analytics
- HPI-DANA-S Spezialisierung
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-C Concepts and Methods
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-T Technologies and Tools
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-S Specialization
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-C Concepts and Methods
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-T Technologies and Tools
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-S Specialization
- DSYS: Data-Driven Systems
- HPI-DSYS-C Concepts and Methods
- DSYS: Data-Driven Systems
- HPI-DSYS-T Technologies and Tools
- DSYS: Data-Driven Systems
- HPI-DSYS-S Specialization
- MALA: Machine Learning and Analytics
- HPI-MALA-C Concepts and Methods
- MALA: Machine Learning and Analytics
- HPI-MALA-T Technologies and Tools
- MALA: Machine Learning and Analytics
- HPI-MALA-S Specialization
Voraussetzungen
Prior familiarity with ML/AI required.
Working on projects with the partner companies requires signing a legal agreement. This is to ensure that they are able to share company-internal information with you. The rights to the code and data that you create remains with the student team, but with the understanding that you will negotiate with the company to also allow them to use it.
Lern- und Lehrformen
This is not a regular lecture course. Rather, the students will engage in practical projects working closely with companies. There will be regular milestones/deadlines throughout the semester. The final presentations are scheduled for the final week of the lecture period. After that, the only remaining deadline is for the final report.
Leistungserfassung
The grades will be based on:
- Project Report (5-10 pages): 80%
- Final Presentation 20%
Termine
Details about the course will be given in the first session (Monday, Oct. 16, 11:00-12:30, HPI Building F, Room F-E.06).
Details of the topics will be presented in the second session (Thursday, Oct. 19, 09:15-10:45, HPI Building F, Room F-E.06). The assignment of topics will then take place in the following days.
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