Machine Learning for Data Science

Terms
  1. Machine Learning for Data Science - WS19/20

Summary

During this course you will get an an understanding of fundamental applications, concepts, and analytical techniques in the area of machine learning for data sciences. You will learn how to design suitable experiments for complex issues and to collect, tap into, store, process, and analyze data. After the course you will know what results can be derived from typical data sources and how to execute and evaluate computer-aided procedures appropriately within the field of application and in the relevant scientific context.

Topics include
  • 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.