phy731 - Wahlpflicht Theorie (Veranstaltungsübersicht)

phy731 - Wahlpflicht Theorie (Veranstaltungsübersicht)

Institut für Physik 6 KP
Modulteile Semesterveranstaltungen Sommersemester 2021 Prüfungsleistung
Vorlesung
  • Kein Zugang 5.04.4215 - Machine Learning II – Advanced Learning and Inference Methods Lehrende anzeigen
    • Prof. Dr. Jörg Lücke

    Donnerstag: 10:00 - 12:00, wöchentlich (ab 15.04.2021)
    Termine am Donnerstag, 29.07.2021 - Freitag, 30.07.2021, Montag, 02.08.2021 08:00 - 19:00

    The students will deepen their knowledge on mathematical models of data and sensory signals. Building up on the previously acquired Machine Learning models and methods, the students will be lead closer to current research topics and will learn about models that currently represent the state-of-the-art. Based on these models, the students will be exposed to the typical theoretical and practical challenges in the development of current Machine Learning algorithms. Typical such challenges are analytical and computational intractabilities, or local optima problems. Based on concrete examples, the students will learn how to address such problems. Applications to different data will teach skills to use the appropriate model for a desired task and the ability to interpret an algorithm’s result as well as ways for further improvements. Furthermore, the students will learn interpretations of biological and artificial intelligence based on state-of-the-art Machine Learning models. Contents: This course builds up on the basic models and methods introduced in introductory Machine Learning lectures. Advanced Machine Learning models will be introduced alongside methods for efficient parameter optimization. Analytical approximations for computationally intractable models will be defined and discussed as well as stochastic (Monte Carlo) approximations. Advantages of different approximations will be contrasted with their potential disadvantages. Advanced models in the lecture will include models for clustering, classification, recognition, denoising, compression, dimensionality reduction, deep learning, tracking etc. Typical application domains will be general pattern recognition, computational neuroscience and sensory data models including computer hearing and computer vision.

  • Kein Zugang 5.04.4586 - Digital Signal Processing Lehrende anzeigen
    • Prof. Dr. Simon Doclo

    Montag: 16:00 - 18:00, wöchentlich (ab 12.04.2021), Ort: (online)
    Termine am Donnerstag, 29.07.2021 10:00 - 12:00, Montag, 04.10.2021 15:00 - 17:00, Ort: A14 1-101 (Hörsaal 1), W32 0-005

    Engineering Physics: Alternative für Signal- und Systemtheorie

Seminar
Übung
  • Kein Zugang 5.04.4012 Ü1 - Informationsverarbeitung und Kommunikation Lehrende anzeigen
    • Priv.-Doz. Dr. Jörn Anemüller
    • Eike Jannik Nustede, M. Sc.

    Donnerstag: 16:00 - 18:00, wöchentlich (ab 15.04.2021)

    Die Studierenden erlernen, wie statistische Eigenschaften von Signalen zur Lösung von Problemen der Angewandten Physik, insbesondere der Klassifikation, parametrischen Modellierung und Übertragung von Signalen genutzt werden können. Theoretische Lernziele beinhalten damit eine Wiederholung und Festigung statistischer Grundlagen und eine Verständnis von deren Nutzung für Algorithmen unterschiedlicher Zielsetzung und Komplexität. Im praktischen Teil werden Eigenschaften der behandelten Methoden selbständig erarbeitet sowie Algorithmen auf dem Rechner implementiert und auf reale Daten angewendet, so daß der Umgang mit theoretischen Konzepten und ihre praktische Umsetzung erlernt werden. Inhalte: Grundfragen der Informationsverarbeitung (Klassifikation, Regression, Clustering), Lösungsmethoden basierend auf Dichteschätzung und diskriminativen Ansätzen (z.B. Bayes Schätzung, k-nearest neighbour, Hauptkomponentenanalyse, support-vector-machines, Hidden-Markov- Modelle), Grundlagen der Informationstheorie, Methoden der analogen und digitalen Nachrichtenübertragung, Prinzipien der Kanalcodierung und Kompression

  • Kein Zugang 5.04.4215 Ü1 - Exercises to Machine Learning II – Advanced Learning and Inference Methods Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Florian Hirschberger
    • Filippos Panagiotou
    • Jakob Drefs
    • Dr. rer. nat. Seyyed Hamid Mousavi Hashemi

    Dienstag: 14:00 - 16:00, wöchentlich (ab 13.04.2021), Übung

    The students will deepen their knowledge on mathematical models of data and sensory signals. Building up on the previously acquired Machine Learning models and methods, the students will be lead closer to current research topics and will learn about models that currently represent the state-of-the-art. Based on these models, the students will be exposed to the typical theoretical and practical challenges in the development of current Machine Learning algorithms. Typical such challenges are analytical and computational intractabilities, or local optima problems. Based on concrete examples, the students will learn how to address such problems. Applications to different data will teach skills to use the appropriate model for a desired task and the ability to interpret an algorithm’s result as well as ways for further improvements. Furthermore, the students will learn interpretations of biological and artificial intelligence based on state-of-the-art Machine Learning models. Contents: This course builds up on the basic models and methods introduced in introductory Machine Learning lectures. Advanced Machine Learning models will be introduced alongside methods for efficient parameter optimization. Analytical approximations for computationally intractable models will be defined and discussed as well as stochastic (Monte Carlo) approximations. Advantages of different approximations will be contrasted with their potential disadvantages. Advanced models in the lecture will include models for clustering, classification, recognition, denoising, compression, dimensionality reduction, deep learning, tracking etc. Typical application domains will be general pattern recognition, computational neuroscience and sensory data models including computer hearing and computer vision.

  • Kein Zugang 5.04.4215 Ü2 - Exercises to Machine Learning II – Advanced Learning and Inference Methods Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Filippos Panagiotou
    • Jakob Drefs
    • Dr. rer. nat. Seyyed Hamid Mousavi Hashemi
    • Florian Hirschberger

    Donnerstag: 10:00 - 12:00, wöchentlich (ab 15.04.2021), Übung

    The students will deepen their knowledge on mathematical models of data and sensory signals. Building up on the previously acquired Machine Learning models and methods, the students will be lead closer to current research topics and will learn about models that currently represent the state-of-the-art. Based on these models, the students will be exposed to the typical theoretical and practical challenges in the development of current Machine Learning algorithms. Typical such challenges are analytical and computational intractabilities, or local optima problems. Based on concrete examples, the students will learn how to address such problems. Applications to different data will teach skills to use the appropriate model for a desired task and the ability to interpret an algorithm’s result as well as ways for further improvements. Furthermore, the students will learn interpretations of biological and artificial intelligence based on state-of-the-art Machine Learning models. Contents: This course builds up on the basic models and methods introduced in introductory Machine Learning lectures. Advanced Machine Learning models will be introduced alongside methods for efficient parameter optimization. Analytical approximations for computationally intractable models will be defined and discussed as well as stochastic (Monte Carlo) approximations. Advantages of different approximations will be contrasted with their potential disadvantages. Advanced models in the lecture will include models for clustering, classification, recognition, denoising, compression, dimensionality reduction, deep learning, tracking etc. Typical application domains will be general pattern recognition, computational neuroscience and sensory data models including computer hearing and computer vision.

  • Kein Zugang 5.04.4586 Ü1 - Exercises to Digital Signal Processing Lehrende anzeigen
    • Daniel Fejgin
    • Klaus Brümann

    Mittwoch: 12:00 - 14:00, wöchentlich (ab 21.04.2021)

    Engineering Physics: Alternative für Signal- und Systemtheorie

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.