The course provides an overview of machine learning methods and algorithms for different learning tasks, namely supervised, unsupervised and ... read more
Qualifikationsziele:
The course provides an overview of machine learning methods and algorithms for different learning tasks, namely supervised, unsupervised and reinforcement learning.
In the first part of the course, for each task the main algorithms and techniques will be covered including experimentation and evaluation aspects.
In the second part of the course, we will focus on specific learning challenges including high-dimensionality, non-stationarity, label-scarcity and class-imbalance.
By the end of the course, you will have learned how to build machine learning models for different problems, how to properly evaluate their performance and how to tackle specific learning challenges.
Inhalte
Es werden Themen aus folgenden Gebieten behandelt:
Experiment Design
Sampling Techniques
Data cleansing
Storage of large data sets
Data visualization and graphs
Probabilistic data analysis
Prediction methods
Knowledge discovery
Neural networks
Support vector machines
Reinforcement learning and agent models
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32 Class schedule
Additional appointments
Tue, 2025-03-11 16:00 - 18:00
Nachklausur
Lecturers:
Prof. Dr.-Ing. Grégoire Montavon
Location:
Hs A (Raum B.006, 200 Pl.) (Arnimallee 22)
Qualifikationsziele:
The course provides an overview of machine learning methods and algorithms for different learning tasks, namely supervised, unsupervised and ... read more