phy731 - Wahlpflicht Theorie (Veranstaltungsübersicht)

phy731 - Wahlpflicht Theorie (Veranstaltungsübersicht)

Institut für Physik 6 KP
Modulteile Semesterveranstaltungen Wintersemester 2022/2023 Prüfungsleistung
Vorlesung
  • Kein Zugang 2.01.597 - Practical Deep Learning in PyTorch Lehrende anzeigen
    • Prof. Dr. Nils Strodthoff
    • Tiezhi Wang
    • Juan Lopez Alcaraz

    Dienstag: 14:15 - 15:45, wöchentlich (ab 18.10.2022), V, Ort: V03 3-A324, V03 0-M017, (V03 00 M030 (A))
    Mittwoch: 16:15 - 17:45, wöchentlich (ab 19.10.2022), Übung, Ort: A02 2-239
    Freitag: 10:15 - 11:45, wöchentlich (ab 21.10.2022), Ort: V03 3-A324, A01 0-004, (V03 00 M030 (A))
    Termine am Donnerstag, 16.02.2023 11:30 - 13:30, Dienstag, 04.04.2023 09:30 - 11:30, Ort: A14 1-103 (Hörsaal 3), A01 0-004

    This lecture will provide a general introduction to modern deep learning methods with a particular emphasis on practical applicability. At the same time, the course will provide an introduction to the popular PyTorch Deep Learning framework while requiring only basic programming skills in Python. The course will cover a range of common machine learning tasks across different data modalities ranging from tabular data over Computer Vision (image classification, image segmentation) to time series and natural language processing. It will cover the most important model architectures in these domains ranging from convolutional neural networks over recurrent neural networks to transformers. The lecture will be accompanied by a tutorial class where students are supposed to acquire hands-on experience in working with PyTorch and are supposed to acquire the skills to apply Deep Learning methods in their respective fields of study.

  • Kein Zugang 5.04.4207 - Processing and analysis of biomedical data Lehrende anzeigen
    • Thomas Brand
    • Dr. Stefan Uppenkamp, Dipl.-Phys.
    • Dr. Stephan Ewert, Dipl.-Phys.

    Montag: 08:15 - 09:45, wöchentlich (ab 17.10.2022), Ort: W03 2-240
    Donnerstag: 08:15 - 09:45, wöchentlich (ab 20.10.2022), Ort: W01 0-008 (Rechnerraum)
    Termine am Montag, 20.02.2023 08:00 - 10:00, Ort: W01 0-015

    This course introduces basic concepts of statistics and signal processing and applies them to real-world examples of bio-medical data. In the second part of the course, recorded datasets are noise-reduced, analyzed, and discussed in views of which statistical tests and analysis methods are appropriate for the underlying data. The course forms a bridge between theory and application and offers the students the means and tools to set up and analyze their future datasets in a meaningful manner. content: Normal distributions and significance testing, Monte-Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confi-dence intervals, Dipole source analysis, Analysis of variance Each technique is explained, tested and discussed in the exercises.

Seminar
Übung
  • Kein Zugang 2.01.597 - Practical Deep Learning in PyTorch Lehrende anzeigen
    • Prof. Dr. Nils Strodthoff
    • Tiezhi Wang
    • Juan Lopez Alcaraz

    Dienstag: 14:15 - 15:45, wöchentlich (ab 18.10.2022), V, Ort: V03 3-A324, V03 0-M017, (V03 00 M030 (A))
    Mittwoch: 16:15 - 17:45, wöchentlich (ab 19.10.2022), Übung, Ort: A02 2-239
    Freitag: 10:15 - 11:45, wöchentlich (ab 21.10.2022), Ort: V03 3-A324, A01 0-004, (V03 00 M030 (A))
    Termine am Donnerstag, 16.02.2023 11:30 - 13:30, Dienstag, 04.04.2023 09:30 - 11:30, Ort: A14 1-103 (Hörsaal 3), A01 0-004

    This lecture will provide a general introduction to modern deep learning methods with a particular emphasis on practical applicability. At the same time, the course will provide an introduction to the popular PyTorch Deep Learning framework while requiring only basic programming skills in Python. The course will cover a range of common machine learning tasks across different data modalities ranging from tabular data over Computer Vision (image classification, image segmentation) to time series and natural language processing. It will cover the most important model architectures in these domains ranging from convolutional neural networks over recurrent neural networks to transformers. The lecture will be accompanied by a tutorial class where students are supposed to acquire hands-on experience in working with PyTorch and are supposed to acquire the skills to apply Deep Learning methods in their respective fields of study.

  • Kein Zugang 5.04.4207 - Processing and analysis of biomedical data Lehrende anzeigen
    • Thomas Brand
    • Dr. Stefan Uppenkamp, Dipl.-Phys.
    • Dr. Stephan Ewert, Dipl.-Phys.

    Montag: 08:15 - 09:45, wöchentlich (ab 17.10.2022), Ort: W03 2-240
    Donnerstag: 08:15 - 09:45, wöchentlich (ab 20.10.2022), Ort: W01 0-008 (Rechnerraum)
    Termine am Montag, 20.02.2023 08:00 - 10:00, Ort: W01 0-015

    This course introduces basic concepts of statistics and signal processing and applies them to real-world examples of bio-medical data. In the second part of the course, recorded datasets are noise-reduced, analyzed, and discussed in views of which statistical tests and analysis methods are appropriate for the underlying data. The course forms a bridge between theory and application and offers the students the means and tools to set up and analyze their future datasets in a meaningful manner. content: Normal distributions and significance testing, Monte-Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confi-dence intervals, Dipole source analysis, Analysis of variance Each technique is explained, tested and discussed in the exercises.

Hinweise zum Modul
Teilnahmevoraussetzungen
Bachelor in Physik, Technik und Medizin oder entsprechender Abschluss
Prüfungsleistung Modul
Klausur (max. 180 Min.) oder mündliche Prüfung (30 Min.) oder Referat (30 Min.) oder Hausarbeit
Kompetenzziele
Die Studierenden erwerben die theoretischen Voraussetzungen für die numerische und analytische Modellierung komplexer Vorgänge in der Medizin, Biologie und Biophysik, und wenden Forschungsmethoden des Exzellenzcluster Hearing4all im Modellierungsbereich an. Spezielle Kompetenzen abhängig von der gewählten Veranstaltung.