250141 VO Applied machine learning (2023S)
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
Language: German
Examination dates
Lecturers
- Lukas Exl
- Norbert Mauser
- Emina Demirovic (Student Tutor)
Classes (iCal) - next class is marked with N
Vorbesprechung 1.3.2023 HS13 16:45
Wednesday
01.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
06.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
08.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
15.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
20.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
22.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
27.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
29.03.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
17.04.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
19.04.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
24.04.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
26.04.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
03.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
08.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
10.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
15.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
17.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
22.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
24.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
31.05.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
05.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
07.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
12.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
14.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
19.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
21.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
26.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
28.06.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
The grade results from the team project and small "exams". Alternative to the presentation there is also the possibility to do an oral exam.
Minimum requirements and assessment criteria
The course imparts basic knowledge about "Machine Learning" using lectures, exercises and a small team project.
Examination topics
Presentation of team project in the end of semester
Reading list
Lecture notes.
Further literature:
Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media, 2019.
Forsyth, David, Applied Machine Learning. Springer International Publishing, 2019.
Shalev-Shwartz, Shai, and Shai Ben-David, Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
Further literature:
Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media, 2019.
Forsyth, David, Applied Machine Learning. Springer International Publishing, 2019.
Shalev-Shwartz, Shai, and Shai Ben-David, Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
Association in the course directory
ZWM
Last modified: We 08.11.2023 08:07
The VU aims to "reconcile" theoretical and application-relevant aspects. In addition to the particularly practically relevant basics such as data analysis, model selection, validation, over- and underfitting, "feature selection", we will discuss "classical methods" such as non-linear model reduction (PCA, MDS, kernel methods, etc.), classification (logistic regression, random forests , SVMs, etc), regression (kernel rigde regression, lasso, etc), clustering, ensemble learning, as well as the basics of the current Deep Neural Networks (DNN), especially new advanced methodologies like autoencoders/-decoders, convolutional Neural Networks and " Physics-Informed Neural Networks (PINN)".
Using practical exercises, the handling of this is taught "hands-on" via Python, scikit-learn and keras/tensor flow, where we use the no-setup environment Kaggle with free GPU access. The VU will also deal with current research topics and applications in physics and materials research.Structure:
The VO "Applied Machine Learning" combines (i) a "theory part" where mathematical and numerical basics of machine learning are presented, (ii) practical exercises that accompany the first part, and (iii) a "group work" where a small application problem in groups of about 2-6 people is considered and presented.!!! There are separate accompanying exercise courses -> registration required: LV-Nr. 250144.