650.025 (22W) Machine Learning and Deep Learning
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.
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 schemePosition 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
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
-
Subject: Artificial Intelligence
(Compulsory elective)
- 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
-
2.2 Machine Learning and Deep Learning (
0.0h VC / 6.0 ECTS)
-
Subject: Artificial Intelligence
(Compulsory subject)
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)