mar768 - Statistical Analysis (Course overview)

mar768 - Statistical Analysis (Course overview)

Institute for Chemistry and Biology of the Marine Environment 6 KP
Module components Semester courses Wintersemester 2022/2023 Examination
Lecture
  • No access 2.01.597 - Practical Deep Learning in PyTorch Show lecturers
    • Prof. Dr. Nils Strodthoff
    • Tiezhi Wang
    • Juan Lopez Alcaraz

    Tuesday: 14:15 - 15:45, weekly (from 18/10/22), V, Location: V03 3-A324, V03 0-M017, (V03 00 M030 (A))
    Wednesday: 16:15 - 17:45, weekly (from 19/10/22), Übung, Location: A02 2-239
    Friday: 10:15 - 11:45, weekly (from 21/10/22), Location: V03 3-A324, A01 0-004, (V03 00 M030 (A))
    Dates on Thursday, 16.02.2023 11:30 - 13:30, Tuesday, 04.04.2023 09:30 - 11:30, Location: 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.
Seminar
Exercises
  • No access 2.01.597 - Practical Deep Learning in PyTorch Show lecturers
    • Prof. Dr. Nils Strodthoff
    • Tiezhi Wang
    • Juan Lopez Alcaraz

    Tuesday: 14:15 - 15:45, weekly (from 18/10/22), V, Location: V03 3-A324, V03 0-M017, (V03 00 M030 (A))
    Wednesday: 16:15 - 17:45, weekly (from 19/10/22), Übung, Location: A02 2-239
    Friday: 10:15 - 11:45, weekly (from 21/10/22), Location: V03 3-A324, A01 0-004, (V03 00 M030 (A))
    Dates on Thursday, 16.02.2023 11:30 - 13:30, Tuesday, 04.04.2023 09:30 - 11:30, Location: 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.
  • No access 5.03.101 - Analyse landschaftsökologischer Daten Show lecturers
    • Dr. rer. nat. Janek Greskowiak
    • Dr. Cord Peppler-Lisbach

    Monday: 14:15 - 17:30, weekly (from 17/10/22)
    Tuesday: 14:15 - 17:30, weekly (from 18/10/22)
    Wednesday: 08:15 - 11:30, weekly (from 26/10/22)
    Dates on Wednesday, 19.10.2022 08:15 - 09:45, Wednesday, 19.10.2022 10:15 - 11:30, Monday, 24.10.2022 14:15 - 15:45, Monday, 24.10.20 ...(more)
Hinweise zum Modul
Prüfungszeiten

Klausur am Ende der Veranstaltungszeit oder alle anderen möglichen Prüfungsleistungen nach Maßgabe der Dozentin oder des Dozenten
Module examination
KL
Skills to be acquired in this module
Die Studierenden besitzen erweiterte Kenntnisse über Analyse- und Modellierungsmethoden von Umweltdaten.