Information Management Course 2014-15

General Informations

Homepage of previous editions

Lecture topics and slides

• Lecture 01 (Oct 2nd '14): Introduction to Information Management and Analytics (slides in LibreOffice and (slides in pdf format); motivation and examples; big data and analytics; main issues in data mining.
• Lecture 02 (Oct 2nd '14): Know your data (slides in LibreOffice and slides in pdf format); data objects and attribute types, basic statistical descriptions of data, measuring data proximity.
• Lectures 03 and 04 (Oct 9th '14): Data preprocessing (slides in LibreOffice and slides in pdf format); dataset for exercises: Breast Cancer Wisconsin . An R IDE can be downloaded from the R Studio homepage .
• Lectures 05 and 06 (Oct 16th '14): R tutorials ( list of topics in pdf format , the R script and function definitions we developed during the lecture, with a few additional examples)
• Lecture 07 (Oct 23rd '14): Data (numerosity) reduction
• Lecture 08 (Oct 23rd '14): Data (dimensionality) reduction - Principal Component Analysis (slides in LibreOffice and slides in pdf format)
• Lecture 09 and 10 (Oct 30th '14): PCA in practice; datasets for exercises: Houses dataset and Forest Fire dataset ; the R code of the PCA implementation we built in the lab session, and a few lines of script to test it.
• Lectures 11 and 12 (Nov 11th) and 13 (Nov 18th): Data (dimensionality) reduction - Feature Selection Algorithms (slides in LibreOffice and slides in pdf format; datasets for experiments ).
• Lecture 14 (Nov 18th): Datawarehousing, Online Analytical Processing and Data Cube Technology overview (slides in LibreOffice and slides in pdf format).
• Lectures 15 and 16 (Nov 25th): Mining frequent patterns (part 1: slides in LibreOffice and slides in pdf format; part 2: slides in LibreOffice and slides in pdf format).
• Lectures 17 and 18 (Dec 2nd): Classification Models and Methods ( General concepts, Decision Tree Induction: slides in LibreOffice and slides in pdf format; Bayesian Classifiers: slides in LibreOffice and slides in pdf format; Support Vector Machines, Evaluating Classification Models: slides in LibreOffice and slides in pdf format; ).
• Lecture 19 (Dec 4th): Information Management in Practice: mining natual language patterns (M. Torabi), Uncertainty and Data Analysis for Large-Scale Energy Systems (A. Taverna).
• Lectures 20 and 21 (Dec 9th): Cluster analysis ( General concepts, Custering methods: slides in LibreOffice and slides in pdf format; Evaluating clustering results: slides in LibreOffice and slides in pdf format; ).
• Lecture 22 (Dec 11th): Information Management in Practice: Data Mining in Mobile Cellular Networks (M. Premoli).
• Lectures 23 and 24 (Dec 16th): Classification and Clustering in practice using R (R code of the lecture).

Pointers to related material

• The reference textbook for the course is "Data Mining: concepts and techniques" by J. Han, M. Kamber and J. Pei (3rd edition).
• Lecture 8 is adapted from "Data Mining methods and models" by D.T. Larose .
• Lectures 11, 12 and 13 are adapted from L.C. Molina, L. Belanche, A. Nebot "Feature Selection Algorithms: a survey and experimental evaluation", proc. of IEEE ICDM 2002 and L. Belanche, F. Gonzales "Review and Evaluation of Feature Selection Algorithms in Synthetic Problems", arxiv 2011.
• The former editions of the Information Management course were held by prof. A. Tettamanzi: you can find syllabus and slides at the corresponding website.