Computational Geosciences is implementing a new course on Data Mining & Practical Machine Learning techniques for geoscientists, including a practical lab.
Description: The course shall present to participants, by means of tutorials and hands-on sessions, tools and techniques to capture and store vast quantities of data; find patterns, trends and anomalies in large datasets; summarize data with simple quantitative models; automatically extract models from data, and validate the extracted models.
Contents (not exhaustive): Structural patterns, Machine Learning and statistics, introduction to concepts, instances and attributes, knowledge representation, decision trees, algorithms (Bayesian models, association rules, instance-based learning, linear models and clustering), evaluation, implementations and transformations. A JAVA-based Graphical-User-Interface software is being planned for the practical labs.
Pre-requisites: Basic knowledge of statistics is required, and you will profit the most from the course if you bring experience with data analysis from your research. The course is primarily intended for Master students and/or Doctoral candidates.
Schedule: Please check course availability and schedule in KLIPS 2.0
Contact: Please contact Dr. Eric Parteli (eric.parteli
uni-koeln.de) if you want to attend to the course, thereby informing: Institute, working group, title of research, highest academic degree, know differentiation / integration (yes/no), know any programming language (if yes, which?).