Technische Universität Ilmenau

Deep Learning - Modultafeln der TU Ilmenau

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Modulinformationen zu Deep Learning im Studiengang Diplom Elektrotechnik und Informationstechnik 2017
Modulnummer200131
Prüfungsnummer220488
FakultätFakultät für Informatik und Automatisierung
Fachgebietsnummer 2234 (SP/JP Softwaretechnik für sicherheitskritische Systeme)
Modulverantwortliche(r)Prof. Dr. Patrick Mäder
TurnusWintersemester
SpracheEnglisch
Leistungspunkte5
Präsenzstudium (h)45
Selbststudium (h)105
VerpflichtungWahlmodul
AbschlussPrüfungsleistung mit mehreren Teilleistungen
Details zum Abschluss

Das Modul Deep Learning mit der Prüfungsnummer 220488 schließt mit folgenden Leistungen ab:

  • alternativ semesterbegleitende Prüfungsleistung mit einer Wichtung von 50% (Prüfungsnummer: 2200822)
  • alternativ semesterbegleitende Prüfungsleistung mit einer Wichtung von 50% (Prüfungsnummer: 2200823)


Details zum Abschluss Teilleistung 1:

  • several assignments evaluating methodological and practical competence in the taught concepts to be solved at home with due date and submission via Moodle
  • number of assigments annoucened in the early lectures of the course
  • partial result determined as average across submitted solutions of assignments
  • students must register via thoska for this exam, typically within the 3rd and 4th week of the semster


Details zum Abschluss Teilleistung 2:

  • written test consisting of mutiple choice and free form questions evaluating the professional conpetence in the topics of the course
  • preferably conducted digitally via Moodle
  • final results may be scaled or individual questions may be excluded depndening on best performing perceptile of students
Anmeldemodalitäten für alternative PL oder SLDie Anmeldung zur alternativen semesterbegleitenden Abschlussleistung erfolgt über das Prüfungsverwaltungssystem (thoska) außerhalb des zentralen Prüfungsanmeldezeitraumes. Die früheste Anmeldung ist generell ca. 2-3 Wochen nach Semesterbeginn möglich. Der späteste Zeitpunkt für die An- oder Abmeldung von dieser konkreten Abschlussleistung ist festgelegt auf den (falls keine Angabe, erscheint dies in Kürze):
max. Teilnehmerzahl
Vorkenntnisse
  • basic programming skills in Python
  • basic understanding of machine learning preferable

Lernergebnisse und erworbene KompetenzenProfessional competence gained through lectures and examined through written exam:
  • Students have knowledge about theoretical foundations of deep neural networks.
  • Students have knowledge about CNN architectes and their applications.
  • Students have knowledge about architectures for sequence modeling and their applications.

Methodological competence gained through seminars and examined through aPl (assignments):
  • Students gained the ability to implement and apply a variety of deep learning algorithms.
  • Students gained the ability to evaluate and troubleshoot deep learning models.
  • Students gained the ability to use computational resources for training and application of deep learning models.

Social competence gained through lectures and seminars:
  • Students gained insights in ethical aspects of machine learning (e.g., bias, autonomous driving) through discussions in lectures and seminars.
  • Students can discussed advantages and disadvantages of different deep learning approaches among each other and with their lectureres and gained professionality in mastering discussions beyond their mother tongue. 
Inhalt

Deep learning has recently revolutionized a variety of application like speech recognition, image classification, and language translation mostly driven by large tech companies, but increasingly also small and medium-sized companies aim to apply deep learning techniques for solving  an ever increasing variety of problems. This course will give you detailed insight into deep learning, introducing you to the fundamentals as well as to the latest tools and methods in this rapidly emerging field.

Deep learning thereby refers to a subset of machine learning algorithms that analyze data in succeeding stages, each operating on a different representation of the analyzed data. Specific to deep learning is the ability to automatically learn these representations rather than relying on domain expert for defining them manually.

The course will teach you the theoretical foundations of deep neural networks, which will provide you with the understanding necessary for adapting and successfully applying deep learning in your own applications. Additionally, by completing the course, you will be able to implement, parametrize and apply a variety of deep learning algorithms. You will learn how to use deep convolutional neural networks (CNNs) as well as recurrent neural networks (RNNs) for image, text, and time series analysis. You will further become familiar with advanced data science tools and in using computational resources to train and apply deep learning models.

Medienformen
  • Projector presentation
  • Slide decks available through Moodle
  • Assignment management through Moodle
  • Cloud services (personal computer required)
Literatur
  • Deep Learning: Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press (2016)

  • Pattern Recognition and Machine Learning: Christoper M. Bishop, Springer (2006)

  • Hands-On Machine Learning with Scikit-Learn and TensorFlow: Aurélien Géron, O'Reilly Media (2017)

Lehrevaluation