Information on individual educational components (ECTS-Course descriptions) per semester

Fundamental concepts of probability theory such as distribution, expectation value, standard deviation, conditional probabilities. Introduction to the tool WEKA (LV 024912010701 Data Organization)

General framework for machine learning, in particular a framework for modelling uncertainty as the starting point for all learning methods used in this course Workflow model for the planning and execution of an ML project Introduction to text and activity classification Algorithms such as Multinomial Bayes, Random Forest, Support Vector Machines, in basic understanding, not in full detail Processing and pre-processing of large data sets, using the two example domains Introduction of a second tool for machine learning (e.g. SPSS).

Students are able to describe how computers can be used to solve tasks for which they have not been explicitly programmed, especially in the field of classification, pattern recognition, meaningful data aggregation (clustering), speech and image recognition, less / only on the edge in the area of intelligent control. address the problem of several, different solutions and how to evaluate them and select one. explain the setup and operationalization of an application in the area of machine learning by means of an example. They can use their first practical experience with text classification / search engines or Human Activity Recognition (HAR). design the process and use the tools to set up and operationalize an application w ithin two different tool environments.

Lecture + in-class exercises, homework every two weeks.

Continual assessment. Evaluation of exercises.

None

• Jurafsky, Daniel ; Martin, James H. (2013): Speech and Language Processing. Pearson New International Edition. Upper Saddle River, NJ u.a.: Prentice Hall International.   
• Labrador, Miguel A. ; Yejas, Oscar D. Lara (2013): Human Activity Recognition: Using Wearable Sensors and Smartphones. Boca Raton: CRC Press Inc.   
• Mitchell, Tom M. (2006):  The Discipline of Machine Learning Pittsburgh: Carnegie Mellon University. Online im Internet: http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf (Zugriff am: 26.10.2016).  
• Witten, Ian H.  u. a. (2016): Data Mining: Practical Machine Learning Tools and Techniques. 4. Aufl. Morgan Kaufmann.

Face-to-face