626.017 (24S) Selected Topics in Machine Learning

Sommersemester 2024

Ende der Anmeldefrist
19.05.2024 23:59

Erster Termin der LV
04.06.2024 11:00 - 15:00 S.1.37 On Campus
Nächster Termin:
04.06.2024 15:00 - 17:30 S.1.05 On Campus

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

Liste der Termine wird geladen...

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

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

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 Benotungsschema

Position im Curriculum

  • Doktoratsprogramm Informatics (SKZ: ---, Version: 17W.1)
    • Fach: Informatics (Pflichtfach)
      • Informatics ( 0.0h XX / 0.0 ECTS)
        • 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS)
  • Masterstudium Informatics (SKZ: 911, Version: 19W.2)
    • 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
  • 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
  • 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)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2023
  • 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
Sommersemester 2022
  • 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
Sommersemester 2021
  • 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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)
Sommersemester 2018
  • 623.131 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
Sommersemester 2017
  • 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2016
  • 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2015
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2014
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2013
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
Sommersemester 2012
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
Sommersemester 2011
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)