Laurea Magistrale (M.Sc.) in Stochastics and Data Science

Introduction to data mining

Introduction to data mining

Academic year 2018/2019

Sommario del corso

• Course objectives
• Results of learning outcomes
• Course delivery
• Learning assessment methods
• Support activities
• Program
• Suggested textbooks and readings
• Notes
• Teaching tools

Course objectives

The objectives of the course will be introduce students to the field of Data Mining and Machine Learning, that merge together competencies of statistics and computer science.

The course will teach the differences between tasks and models and will introduce the students to some of the popular models in Machine Learning such as binary classification and related tasks, transformation of a binary classification model into a multiple class model, concept learning by means of logical formulas, tree models and their purposes, rule models, subgroup discovery, linear models (least squares, regression), perceptron, Support Vector Machines, Kernel methods.

The course will introduce the algorithms for the training of the models.

The laboratory part of the course will introduce the students to a practical open software suite that includes the algorithms of learning of the models seen during the course (and much more). 

Results of learning outcomes

The results of the learning outcomes will be mastering some the main concepts in Data Mining and Machine Learning and using them in the context of a practical open software suite for data analysis and machine learning. 

Course delivery

The course lessons will be both theoretical and practical (laboratory sessions)

The classes in this course (6 CFU) are borrowed from the classes offered in the first part of another, larger course (9 CFU), named Apprendimento Automatico (Machine Learning) at the Computer Science Department.
The classes of this course will be all the classes of the initial part of the larger course (they are overall, the first 24 classes of the complete course).

These classes are divided into 18 theoretical classes and 6 laboratory sessions.

Learning assessment methods

The final exam will be oral in which the students will be asked to show that they master the theorical lessons (knowledge of the models and of their purposes) and use of the practical software suite (Weka) for data analysis in some use cases.

Support activities

 Machine learning experiments in Laboratory with a software suite for Data Mining.

The laboratory will be a practical support to the learning of the theorical lessons by means of practical data analysis assignments on public data-sets (UCI KDD Archive and Kaggle, a platform for Data Science challenges).

Program

Tasks and models; Binary classification and related tasks; Beyond binary classification (transformation of a binary classification model into a multiple class model; Concept learning by means of logical formulas; Version Space; learning hypothesis by means of Horn clauses; Tree models (decision trees, regression trees, features trees, ranking trees); rule models (list of rules and sets of rules); subgroup discovery;  linear models (least squares, regression); perceptron; Support Vector Machines; Kernel methods;

• The Art and Science of Algorithms that Make Sense of Data
  Author: Peter FlachEdition: First Edition
  Publisher: Cambridge University Press
  ISBN: 9780511973000
  Url: https://www.cambridge.org/core/books/machine-learning/621D3E616DF879E494B094CC93ED36A4
• Documentation of Scikit Learn software suite:
  https://scikit-learn.org/stable/documentation.html

Class schedule

Note

This course is borrowed from Machine Learning and will be delivered at Apprendimento Automatico / Introduction to Data Mining, a.a. 18/19

This course (6 CFU) includes the classes offered in the first part of a larger course (of 9 CFU), named Apprendimento Automatico (Machine Learning) held at the Computer Science Department.
The classes in this course will include all the classes in the first part of the larger course (they are the first 24 classes of the complete course). The classes are divided in 18 theoretical classes and 6 laboratory sessions.

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums