Students know a selection of basic machine learning processes. They can evaluate the results of machine learning algorithms. They are familiar with the fundamentals of probability theory, including conditional probability. They can use machine learning tools.

None

The methods of machine learning and data mining are central topics in current data science research and are already in use in various applications.
Content:

• Introduction to calculating probability
• Representation and deduction of knowledge by means of Naive Bayes
• Identifying patterns
• Evaluating machine learning results

• Skiena S. (2017): Data Science Design. Springer Verlag
  
• Lantz B. (2013): Machine Learning with R, PACKT
  
• Statistical Thinking for Data Science and Analytics, edX course, in "https://www.edx.org/course/statistical-thinking-for-data-science-and-analytics"

Lecture, seminar, guest lectures, supervised group work (2-4 students), small projects in the module referring to the case study

Homework 30%, project presentation 30% and final exam 40%