626.017 (24S) Selected Topics in Machine Learning
Überblick
- Lehrende/r
- LV-Titel englisch Selected Topics in Machine Learning
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Blended-Learning-Lehrveranstaltung
- Online-Anteil 25%
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 19 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Englisch
- LV-Beginn 04.06.2024
Zeit und Ort
LV-Beschreibung
Lehrmethodik inkl. Einsatz von eLearning-Tools
Lectures, practical exercises, and an optional project possibly chosen by the student and a topic of the student's choice.
Inhalt/e
Reinforcement learning
Reinforcement learning is about making sequences of decisions
Stunning achievements of reinforcement learning
How to find good sequences of decisions in an unknown domain through exploration and learning?
Delayed rewards, long-term benefits of decisions, exploration and exploitation
Improving decision policy through exploration
Generalizing what has been learned
Learning from examples and background knowledge
How to use prior knowledge in Machine Learning?
Learning in logic – Inductive Logic Programming (ILP)
Algorithms for learning programs from examples in ILP
Discovering new abstract concepts
Learning qualitative models with applications in robotics
How to model qualitatively, avoiding numbers?
Reasoning and simulation with qualitative models
Learning qualitative models from observations
Learning and planning of robot tasks: rescue robot, cart-pole balancing, humanoid robot, quadcopter
Learning from noisy data
Problems with noise in learning data
Key ideas to cope with noise: simpler models are often better
Algorithms for learning decision trees from noisy data
How to estimate probabilities in machine learning correctly?
Argument-Based Machine Learning (ABML)
Human expert may help learning by annotating training examples with arguments
An algorithm for learning rules from argumented examples
Discovering problem structure with function decomposition
The idea of structuring the learning problem with function decomposition
Discovering structure with HINT algorithm
Improving accuracy and interpretability by structure learning
Prüfungsinformationen
Prüfungsmethode/n
Written exam, possible bonus points for optional project
Prüfungsinhalt/e
Content actually covered in lectures
Beurteilungskriterien/-maßstäbe
Points scored at written exam, adding bonus points from optional project
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Doktoratsprogramm Informatics
(SKZ: ---, Version: 17W.1)
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Fach: Informatics
(Pflichtfach)
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Informatics (
0.0h XX / 0.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS)
-
Informatics (
0.0h XX / 0.0 ECTS)
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Fach: Informatics
(Pflichtfach)
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
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Fach: Artificial Intelligence
(Wahlfach)
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2. Semester empfohlen
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
-
Fach: Artificial Intelligence
(Wahlfach)
- Masterstudium Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 2., 3. Semester empfohlen
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
- Doktoratsstudium Doktoratsstudium der Technischen Wissenschaften
(SKZ: 700, Version: 18W.1)
-
Fach: Studienleistungen gem. § 3 Abs. 2a des Curriculums
(Pflichtfach)
-
Studienleistungen gem. § 3 Abs. 2a des Curriculums (
0.0h XX / 32.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS)
-
Studienleistungen gem. § 3 Abs. 2a des Curriculums (
0.0h XX / 32.0 ECTS)
-
Fach: Studienleistungen gem. § 3 Abs. 2a des Curriculums
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
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Sommersemester 2023
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2022
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2021
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2020
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2019
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2018
- 623.131 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2017
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2016
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2015
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2014
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2013
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
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Sommersemester 2012
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
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Sommersemester 2011
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)