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Introduction to Deep Learning

(in German: Introduction to Deep Learning )

Module-ID: FIN-INF-140013
Link: LSF
Responsibility: Sebastian Stober
Lecturer: Sebastian Stober
Classes:
  • Lecture Introduction to Deep Learning
  • Exercise class Introduction to Deep Learning
 
Applicability in curriculum: - M.Sc. INF: Informatik
- M.Sc. INGINF: Informatik
- M.Sc. WIF: Informatik
- M.Sc. DKE: Learning Methods and Models for Data Science
- M.Sc. DE: Methoden der Informatik
- M.Sc. VC: Computer Science

Abbreviation

IDL

Credit Points

6

Semester

Summer

Term

ab 1.

Duration

1 Semester

Language

english

Level

Master

Intended learning outcomes:
Students ...

  • confidently apply DL techniques to develop a solution for a given problem
  • follow recent DL publications and critically assess their contributions
  • formulate hypotheses and design & conduct DL experiments to validate them
  • document progress & design decisions for reproducibility and transparency
  • have advanced competencies in scientific research in topics of the module

Content:

  • Artificial neural network fundamentals (gradient descent & backpropagation, activation functions)
  • Network architectures (Convolutional Neural Networks, Recurrent/Recursive Neural Networks, Auto-Encoders, Transformers)
  • Regularization techniques
  • Introspection & analysis techniques
  • Optimization techniques
  • Advanced training strategies (e.g. teacher-student)

Workload:

  • 56h contact hours (2 SWS lecture + 2 SWS exercise groups)
  • 124h self study (reading assignments (flipped classroom), programming exercises)

Pre-examination requirements: Type of examination: Teaching method / lecture hours per week (SWS):

Exam requirements: participation and active involvement in the course and the exercises (defined in the 1st lecture and published on the course website) Final exam: written (120 minutes) Schein: pass final exam (at least 4.0)

  • Lecture (2 SWS)
  • Exercise (2 SWS)
Prerequisites according to examination regulations: Recommended prerequisites:

keine

Machine Learning (required)

Media: Literature:

Ian Goodfellow, Yoshua Bengio & Aaron Courville: "Deep Learning", MIT Press, 2016.

Comments:

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