TH Köln

Master Digital Sciences

Dokumente zur Akkreditierung des Studiengangs

Modul »Advanced Machine Learning« (AML)

Informationen zur Organisation des Moduls

Prof. Dr. Gernot Heisenberg (Fakultät F03)
Prof. Dr. Gernot Heisenberg (Fakultät F03), Prof. Dr. Konrad Förstner (Fakultät F03)
Angeboten im
Wintersemester (Dauer 1 Semester)
Campus Köln Süd, oder remote
Anzahl Teilnehmer*innen
minimal 5, maximal 20
Coding Skills in Python
Gesamtaufwand 180h
48h (24h Vorlesung / 24h Übung)
Semesterbegleitendes Projekt mit Fachgespräch
Vermittelte Kompetenzen
Analyze Domains, Model Systems, Implement Concepts, Optimize Systems
Beziehung zu globalen Studiengangskriterien
Interdisziplinarität, Digitalisierung, Transfer

Beitrag zu Handlungsfeldern

Nachfolgend ist die Zuordnung des Moduls zu den Handlungsfeldern des Studiengangs aufgeführt, und zwar als anteiliger Beitrag (als ECTS und inhaltlich). Dies gibt auch Auskunft über die Verwendbarkeit des Moduls in anderen Studiengängen und über die Beziehung zu anderen Modulen im selben Studiengang.

Handlungsfeld ECTS (anteilig) Modulbeitrag zum Handlungsfeld
Generating and Accessing Knowledge 3

This specialization recaps quickly the machine learning and especially deep learning principles, then dives deeper into current topics of the field.

Architecting and Coding Software 2

This module includes software development (notebooks)

Acting Responsibly 1

This module teaches how to deal with data and knowledge generating methods responsibly, acounting for ethics, privacy and security.

Learning Outcome

This specialization recaps quickly the machine learning and especially deep learning principles.

The student dives into the following topics

  • Advanced Feature Engineering Methods
    • Anomaly detection
    • Autoencoders
  • Generative Models
    • Variational Autoencoders
    • Generative Adversarial Networks
  • Explainable Machine Learning
  • Reinforcement learning

by filling their knowledge gaps between theory and practice while applying the methods in python solving natural language understanding and special computer vision real-world problems

for being able to apply modern machine learning methods in enterprises and research and understand the caveats of real-world data and settings.

Inhaltliche Beschreibung des Moduls

  • ML and DL principles (recap)
  • Advanced Feature Engineering Methods
    • Anomaly detection
      • Standardization,Box Plots,Correlation,DB-Scan Clustering,Isolation Forest,Robust Random Cut Forest
    • Autoencoders
      • feature selection and feature extraction
      • Latent variables and spaces
      • Image denoising
      • Missing value imputation / image impainting
      • Domain adaptation
  • Generative Models
    • Variational Autoencoders
    • Generative Adversarial Networks
  • Explainable Machine Learning
    • XAI methods and definitions
    • Partial Dependence Plots
    • Individual Conditional Expectation
    • Centered Individual Conditional Expectation
    • Derivative Individual Conditional Expectation
    • Shapley Values
    • Local Interpretable Model-agnostic Explanations (LIME)
  • Reinforcement learning
    • Definitions
    • Reinforcement control loop
    • Markov Decision process
    • Transition Probabilities
    • Discounted and Expected Return
    • Policies And Value Functions
    • The exploration-exploitation dilemma
    • Q-Learning
    • Deep Reinforcement Learning

Lehr- und Lernformen

  • Lecture
  • Exercises and software development (notebooks)
  • Accompanying project work by analyzing data sets

Zur Verfügung gestelltes Lehrmaterial

  • List of selected literature and web resources
  • Lecture slides
  • Video tutorials
  • Exercises and code tutorials
  • Example code and notebooks on github and Colab
  • Data sets and models

Weiterführende Literatur

  • Ian Goodfellow, Yoshua Bengio und Aaron Courville: Deep Learning (Adaptive Computation and Machine Learning), MIT Press, Cambridge (USA), 2016. ISBN 978-0262035613 .
  • Neural Networks and Deep Learning by Michael Nielsen, ONline Book,
  • Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. “The elements of statistical learning”. (2009)
  • Doshi-Velez, Finale, and Been Kim. “Towards a rigorous science of interpretable machine learning,” no. Ml: 1–13. (2017)