650.025 (20W) Machine Learning and Deep Learning

Wintersemester 2020/21

Registration deadline has expired.

First course session
28.10.2020 14:00 - 18:00 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 Machine Learning and Deep Learning
Type Lecture - Course (continuous assessment course )
Course model Online course
Hours per Week 4.0
ECTS credits 6.0
Registrations 20 (30 max.)
Organisational unit
Language of instruction Englisch
Course begins on 28.10.2020
eLearning Go to Moodle course
Remarks (english)

Due to the synchronization with the introduction to statistics and linear algebra the lectures will start from 28.10.2020

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

The course provides a practical introduction into machine learning methods with the focus on deep learning. 

Teaching methodology including the use of eLearning tools

Lectures with practical sessions and a student's project applying machine learning to a practical problem.

Course content

  • Introduction to AI and machine learning
  • Machine learning preliminaries
  • Basic ML approaches 
  • Artificial Neural Networks
  • Deep Learning Architectures
  • Applications

Prior knowledge expected

The course assumes the basic prior knowledge of the probability theory, linear algebra, and optimization methods. Knowledge of Python programming language is a plus.

Curricular registration requirements

No prerequisites

Literature

Course book:

Extra literature for beginners:

  • James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer
  • Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.

Extra literature - classics:

  • Mitchell, T. (1997) Machine Learning. McGraw Hill. (a bit old, but still the best intro to ML for computer scientists)
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning.  2nd edition, Springer.


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.

Modified examination information (exceptional COVID-19 provisions)

Presentations of projects are done online

Examination methodology

Grades are given based on a project:

  • implement a practical project applying machine learning techniques presented in the course
  • the project is to be accomplished by a group of student comprising max. 2 participants
  • each group prepares only one final presentation of their project, during which every student must be ready to answer any question regarding the presented work
  • expected and accomplished tasks of every student in the project should be clearly indicated in both project proposal and report, respectively - each group participant should submit a separate report describing his/her work 
  • every student must accomplish at least one task which is clearly related to machine learning

Examination topic(s)

Theoretical and practical aspects of techniques used in the project report and the presentation. 

Assessment criteria / Standards of assessment for examinations

Grades are given based on the project presentation (50%) and the report (50%).

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Master's degree programme Artificial Intelligence and Cybersecurity (SKZ: 993, Version: 20W.1)
    • Subject: Artificial Intelligence (Compulsory subject)
      • 2.2 Machine Learning and Deep Learning ( 0.0h VC / 6.0 ECTS)
        • 650.025 Machine Learning and Deep Learning (4.0h VC / 6.0 ECTS)
          Absolvierung im 1. Semester empfohlen

Equivalent courses for counting the examination attempts

Wintersemester 2024/25
  • 650.025 VC Machine Learning and Deep Learning (ML-LV aus Udine) (4.0h / 6.0ECTS)
Wintersemester 2023/24
  • 650.025 VC Machine Learning and Deep Learning (4.0h / 6.0ECTS)
Wintersemester 2022/23
  • 650.025 VC Machine Learning and Deep Learning (4.0h / 6.0ECTS)
Wintersemester 2021/22
  • 650.025 VC Machine Learning and Deep Learning (4.0h / 6.0ECTS)