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

Introduction to data mining

Introduction to data mining

Academic year 2022/2023

Sommario del corso

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

Course objectives

The course introduces students to the fields of Data Mining and Machine Learning, that merge together competencies of statistics and computer science.

The course teaches the differences between tasks and models and introduces the students to some of the popular models in Data Mining such as:

• binary classification and related tasks, 
• tree models and rule models
• association analysis
• Support Vector Machines and Kernel methods.
• Clustering
• Anomaly detection
• Fairness and privacy

The laboratory part of the course introduces the students to python libraries and proposes programming excercises to further develop the understanding of the presented concepts and algorithms.

Results of learning outcomes

Knowledge and understanding: students will be mastering some the main concepts in Data Mining and Machine Learning.

Applying knowledge and understanding: the students will be able to use the learnt knowledge in the context of modern languages and libraries with the goal to solve some real data science tasks (such as classifying examples into a set of given classes, clustering data into meaningful groups, etc.). 

Course delivery

The course will be held in presence.

Lectures will cover theoretical (18 lectures, 36 hours) and practical grounds (6 laboratory sessions, 12 hours).

Laboratory sessions will make use of Jupyter and leverage the Python language. A basic knowlege of python working is assumed.

Learning assessment methods

The final exam will be written with an optional oral examination.

All exams will be held in presence.

The written test will assess the understanding of the presented concepts as well as the student ability to solve small practical problems. The written test will be graded up to 27 points out of 30. The optional oral examination will provide extra grade points in the range -27, +5.

Program

1. Introduction
2. Data
   1. types of data
   2. data quality
   3. data pre-processing
   4. similarity and dissimilarity
   5. avoiding false discoveries
3. Classification
   1. basic concepts
   2. decision trees
   3. model evaluation
4. Association Analysis
   1. frequent itemsets generation
   2. rule generation
   3. compact representation of frequent itemsets
5. Advanced Classification
1. rule-based classifiers
2. nearest neighbors classifier
3. support-vector machines
6. Clustering
   1. overview
   2. k-means
   3. hierarchical clustering
   4. db-scan
   5. cluster evaluation
7. Graph-based Clustering
8. Anomaly detection
   1. clustering based approach
   2. one-class classification
9. Fairness and Privacy

• Documentation of Scikit Learn software suite:
  https://scikit-learn.org/stable/documentation.html

Class schedule

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