650.025 (20W) Machine Learning and Deep Learning
Overview
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
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:
- Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning Cambridge: MIT press. (Available online: https://www.deeplearningbook.org/)
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
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 schemePosition in the curriculum
- Master's degree programme Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
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Subject: Artificial Intelligence
(Compulsory subject)
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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
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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 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)