626.017 (22S) Selected Topics in Machine Learning

Sommersemester 2022

Registration deadline has expired.

First course session
30.05.2022 10:00 - 12:00 online Off Campus
... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.
Lecturer
Course title german Selected Topics in Machine Learning
Type Lecture - Course (continuous assessment course )
Course model Online course
Hours per Week 2.0
ECTS credits 4.0
Registrations 14 (30 max.)
Organisational unit
Language of instruction English
possible language(s) of the assessment English
Course begins on 30.05.2022
eLearning Go to Moodle course

Time and place

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Course Information

Course content

Reinforcement learning

How to find good sequences of decisions in an unknown domain through exploration and learning?

Stunning success of AlphaZero using reinforcement learning

Delayed rewards, long-term benefits of decisions, exploration and exploitation

Improving decision policy through exploration

Generalising what has been learned

Machine learning from noisy data

Problems with noise in learning data

Key ideas to cope with noise: paradoxically, 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

ABML knowledge-elicitation loop

Learning qualitative models with applications in robotics

How to model qualitatively, avoiding numbers

Reasoning and simulation with qualitative models

Learning qualitative models from observations with QUIN and Pade

Learning and planning of robot tasks: rescue robot, humanoid robot, quadcopter

Learning from examples and background knowledge

How to use prior knowledge in Machine Learning?

Learning in logic – Inductive Logic Programming (ILP)

Learning programs from examples with ILP

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 in practice




Examination information

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.

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Thematic Doctoral Programme Informatics (SKZ: ---, Version: 17W.1)
    • Subject: Informatics (Compulsory subject)
      • Informatics ( 0.0h XX / 0.0 ECTS)
        • 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS)
  • Master's degree programme Informatics (SKZ: 911, Version: 19W.2)
    • Subject: Artificial Intelligence (Compulsory elective)
      • 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
  • Master's degree programme Informatics (SKZ: 911, Version: 19W.2)
    • Subject: Data Science and Engineering (Compulsory elective)
      • 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
  • Master's degree programme Informatics (SKZ: 911, Version: 19W.2)
    • Subject: Data Science and Engineering (Compulsory elective)
      • Weitere LVen aus dem gewählten Spezialisierungsfach oder den anderen Spezialisierungsfächern ( 0.0h XX / 16.0 ECTS)
        • 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS)
          Absolvierung im 1., 2. Semester empfohlen
  • Doctoral programme Doctoral programme in Technical Sciences (SKZ: 700, Version: 18W.1)
    • Subject: Studienleistungen gem. § 3 Abs. 2a des Curriculums (Compulsory subject)
      • 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)

Equivalent courses for counting the examination attempts

Sommersemester 2024
  • 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
Sommersemester 2023
  • 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)