700.197 (21S) Tutorium in Machine Learning and Tensorflow Basics

Sommersemester 2021

Registration deadline has expired.

First course session
02.03.2021 17:00 - 19:00 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.
Tutor/Tutors
Course title german Tutorium in Machine Learning and Tensorflow Basics
Type Tutorium
Course model Online course
Hours per Week 2.0
ECTS credits 0.0
Registrations 16
Organisational unit
Language of instruction English
Course begins on 02.03.2021
eLearning Go to Moodle course

Time and place

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
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Course Information

Intended learning outcomes

Get to know and use Github, Pyhton and Machine Learning and Tensorflow Basics.

Teaching methodology including the use of eLearning tools

In the BBB room with theory and programming examples.

Course content

  • Basics of the PYTHON language
  • Overview of the main librairies: Tensorflow, Keras, Pytorch, ...
  • Overview of main frameworks & models: Alexnet, VGG, ...
  • Introduction to GitHub
  • Input / Output management or accesses in Python
  • Short introduction to GAN (Generative Adversarial networks)
  • A few programming examples + small homework exercises

Prior knowledge expected

No previous experience required

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

  • Bachelorstudium Informationstechnik (SKZ: 289, Version: 17W.1)
    • Subject: Informationstechnische Vertiefung (Compulsory elective)
      • 10a.1 Grundlagen und Methoden der Simulationstechnik ( 0.0h VC / 3.0 ECTS)
        • 700.197 Tutorium in Machine Learning and Tensorflow Basics (2.0h TU / 0.0 ECTS)
          Absolvierung im 4. Semester empfohlen
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.197 Tutorium in Machine Learning and Tensorflow Basics (2.0h TU / 0.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.197 Tutorium in Machine Learning and Tensorflow Basics (2.0h TU / 0.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Autonomous Systems and Robotics: Advanced (ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
        • 700.197 Tutorium in Machine Learning and Tensorflow Basics (2.0h TU / 0.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Free Electives (Optional subject)
      • Free Electives ( 0.0h XX / 6.0 ECTS)
        • 700.197 Tutorium in Machine Learning and Tensorflow Basics (2.0h TU / 0.0 ECTS)

Equivalent courses for counting the examination attempts

This course is not assigned to a sequence of equivalent courses