AI-2 Systems Project (AI2SysPro)10 ECTS
(englische Bezeichnung: AI-2 Systems Project)
(Prüfungsordnungsmodul: AI-2 Systems Project)

Lehrveranstaltungen:

• • AI-2 Systems Project
    (Projektseminar, 4 SWS, Michael Kohlhase et al., Di, 14:15 - 15:45)

Empfohlene Voraussetzungen:

Strong programming skills in any programming language.

Inhalt:

This project complements the statistical AI and Machine Learning methods covered in the AI-2 Lecture with practical experience in implementing the algorithms or applying existing state-of-the-art libraries and systems. Practical areas of covered in this project include

• Probabilistic Reasoning

• Bayesian/Decision Networks

• Temporal Probability Models

• MDP/POMDPs

• Inductive Learning

• Neural Networks

• Statistical Learning

• Natural Language Processing
  

Project participants will work on multiple concrete problems/applications in small project teams. The results will be

• evaluated using benchmark/problems and/or competitions between project teams.

• documented in (short) project reports and be presented to the group.
  

Lernziele und Kompetenzen:

Students will

• develop and implement algorithms or apply existing systems/libraries to multiple symbolic AI Problems,

• acquire hands-on experience in an established research field,

• learn best practices in software development and documentation,

• gain first experience in academic writing.

Organisatorisches:

Method of Examination: Working software, competition/benchmark results, project reports, presentations (for all sub-projects).
Grading: Software/Results: 60%, 25% project report, 15% presentation.

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Artificial Intelligence (Master of Science)
   (Po-Vers. 2021s | TechFak | Artificial Intelligence (Master of Science) | Gesamtkonto | Projekt I und II | Subsymbolic AI/Machine Learning | AI-2 Systems Project)

Studien-/Prüfungsleistungen:

AI-2 Systems Project (Prüfungsnummer: 31171)

Prüfungsleistung, Praktikumsleistung, benotet, 10 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
Method of Examination: Working software, competition/benchmark results, project reports, presentations (for all sub-projects). Grading: Software/Results: 60%, 25% project report, 15% presentation.

Prüfungssprache: Englisch

Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023

1. Prüfer:

Michael Kohlhase