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