Teaching

Lecture 2 hours per week, 1 hour seminar and project (a schedule tba)

Project - 1st part (in April, max. 30 points)

Final exam (IS MU, max 40 points)

Project (max 30 points)

<45 F, <54 E, <63 D, <72 C, <81 B, >=81 A

H. J. Miller and J. Han (eds.): Geographic Data Mining and Knowledge Discovery

T. Mitchell, Machine Learning (book and slides)

Slides (here)

Weka (homepage) (datasets) (wiki) (how do I use WEKA's classes in my own code)

• Introduction
• Knowledge discovery and data mining
  Process of knowledge discovery
  Basic tasks
  Data preprocessing
  Data mining and machine learning
  Privacy-preserving data mining
• Introduction to machine learning
  Supervised learning, classification and prediction
  Unsupervised learning, clustering
  Descriptive methods, frequent patterns, association rules and Apriori algorithm (intro) and example (1) (2) (3)
  Learning in predicate logic, classification, frequent pattern discovery
• Tools
  Weka, R, Aleph, RAP
• Logics and learning
  Introduction to modal logic
  Spatio-temporal logics
  Inductive inference in spatial and spatio-temporal data
• Mining in structured data
  Text mining
  Mining in streams
  Mining in object-oriented data
• Postgres and PostGIS
• XML and geographic data, GML, KML, PMML, VRML
• Data structures
• Geographic data mining
  Mining in raster data
  GRR
  Automatická klasifikace a relace sousednosti. Pżípadová studie
  Mining in vector data
  WiM and GWiM
  SPADA
  Mining in spatio-temporal logic
• Visualization
  Visualization and data mining
  CommonGIS (short) (manual)
• Applications
  Mining in remote sensing data
  Mining in historical wind storm data
  Knowledge discovery in crisis management
• Challenges
  Adaptive visualization and machine learning
  Spatio-temporal information extraction from text

Introduction to Spatial Data Mining
Spatial Data Mining by Antti Leino
GIScience conference calendar(Zurich, maintained by Pia Bereuter)
Laboratoż geoinformatiky UJEP