Information-theoretic methods for data exploration

Lecturer

Tijl De Bie, Ghent university, department of Electronics and information systems and Jefrey Lijffijt, Ghent University, department of Electronics and information systems. 

Schedule and place

This 15-hour course will take place at KU Leuven: dates to be determined 

Description

A key problem in the design of methods for exploring data is the quantification of how interesting a 'pattern'  (i.e. a formally described nugget of information) found in data is to the data analyst. This lecture series will describe a framework, called FORSIED  (Formalizing Subjective Interestingness in Exploratory Data mining), for achieving this. The lectures will describe the framework along with a range of instantiations therefor for a broad range of data and problem types.

The FORSIED framework, a high-level overview

   * The FORSIED framework

   * Community detection

   * Perspectives for privacy and fairness

Exploration of discrete data

   * Itemset mining

   * Relational pattern mining

   * Cycles, trees, and forests in graphs

   * Exploring attributed graph data

Exploration of real-valued data

   * Projections

   * Subgroup discovery

   * ct-SNE

   * Time series

Graphs and graph embeddings

   * Advanced MaxEnt models

   * CNE

   * ExplaiNE

   * EvalNE

   * DeBayes

   * ALPINE

   * FONDUE

Course material

To be determined

Evaluation

Homework/projects