Soft Computing - SS21

Temporal and Spatial Data Mining - SS21

The lecture deals with the basics of pattern recognition in time series (e.g., sensor signals) and spatially distributed gathered data (e.g., in sensor networks). Among others, the following topics are covered:

  • Fundamentals (e.g., segmentation of time series, correlation of data, features to describe temporal/spatial data)
  • similarty measurement of time series
  • clustering/classification
  • forecasting
  • motif recognition
  • anomaly detection with various techniques (e.g., Nearest Neighbor, Support Vector Machines)
  • various example applications (signature verification, collaborative hazard warning in vehicles, activity recognition, context recognition with smartphones, and others)

Moreover, we cover novel topics related to the deep-learned-based processing of time series. This includes the fundamentals of deep learning for time series, convolutional and recurrent neural networks to model time series, and other state-of-the-art techniques for processing time series with deep learning methods.


Pattern Recognition and Machine Learning II - SS21

The lecture covers techniques of pattern recognition and machine learning, especially from a probabilistic point of view. It is a follow-up to the lecture Pattern Recognition and Machine Learning I.

The following topics are covered:
  • Gaussian processes,
  • Support Vector Machines,
  • Mixture Models and Expectation Maximization,
  • Variational Inference,
  • Sampling Methods,
  • Continuous latent variables,
  • and Ensembles.

IES-Seminar SS21

Data Mining

Dieser Kurs umfasst die Veranstaltungen "Data Mining in technischen Anwendungen" und "Labor Data Mining und Maschinelles Lernen".

Intelligent Humanoid Robots - SS 2021

Labor C/Embedded Systems - Teil 2

Labor C/Embedded Systems - Teil 1 - 2021