650.025 (22W) Machine Learning and Deep Learning

Wintersemester 2022/23

Registration deadline has expired.

First course session
05.10.2022 14:00 - 15:30 Z.0.01 On 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 Attendance-based course
Hours per Week 4.0
ECTS credits 6.0
Registrations 40 (30 max.)
Organisational unit
Language of instruction Englisch
Course begins on 05.10.2022
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

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

Teaching methodology

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

Basic knowledge of linear algebra, probability theory, and calculus

Literature

Course books:

  • Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning  Cambridge: MIT press. (Available online: https://www.deeplearningbook.org/)
  • Aston Zhang A., Lipton, Z.C., Li M., & Smola A.J. Dive into Deep Learning (2020) (Available online: https://d2l.ai/)

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.

Please consider visiting also the practical course 623.625 "Machine Learning and Deep Learning" of Pierre Tassel, which provides an introduction to various aspects of programming deep neural networks with PyTorch.

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

  • 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)
        • 650.025 Machine Learning and Deep Learning (4.0h VC / 6.0 ECTS)
          Absolvierung im 1., 2. Semester empfohlen
  • 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 2021/22
  • 650.025 VC Machine Learning and Deep Learning (4.0h / 6.0ECTS)
Wintersemester 2020/21
  • 650.025 VC Machine Learning and Deep Learning (4.0h / 6.0ECTS)