Course Syllabus
Obiettivi
To develop skills for solving real worls unsupervised learning problems.
The goal is achieved by;
- Teaching how to design, train, deploy and monitor unsupervised learning models.
- Exploiting open source platforms, languages and software,
- Stimulating team working.
Contenuti sintetici
The course contents are the following;
- Data Types; to list different types of data and to learn hw they must be used for unsupervised learning.
- Data Preprocessing; to preprocess data in such a way it can be used by unsupervised learning tasks,
- Clustering Learning; to form homogeneous groups of observations and/or attributes using a given proximity measure,
- Clustering Validation; to evaluate and compare diferent clusteirng solutions to select the one to deploy.
- Anomaly Detection; to find anomalous observations, to discover outliers observations, under different theoretical settings.
- Bayesian Networks; to learn probabilistic/causal structure from data and to make decisions under uncertainty.
You will learn how to design, train, validate and deploy unsupervised learning models using different programming languages; mainly Python and R.
Programma esteso
1. Data
1.1 Data types and attributes
1.2 Proximity measures for nominal, ordinal and continuous attributes
1.3 Data Pre-Processing
2. Cluster Analysis
2.1 Introduction
2.2 Clustering algorithms
2.2.1 Partitioning
2.2.2 Hierarchical
2.2.3 Graph-based
2.2.4 Density-based
2.2.5 Time-series
2.3 Comparing clustering solutions
2.3.1 Performance measures
2.3.2 Evaluation
2.3.3 Comparison
3. Anomaly Detection
3.1 Introduction
3.2 Anomaly detection algorithms
3.2.1 Statistical approaches
3.2.2 Proximity-based approaches
3.2.3 Clustering-based approaches
3.2.4 One-class classification
3.2.5 Information theoretic approaches
4. Bayesian Networks
4.1 Introduction
4.2 Bayesian network models
4.2.1 Discrete variables
4.2.2 Continuous variables
4.2.3 Mixed variables
4.3 Learning
4.3.1 Parameters
4.3.2 Structure
4.4 Inference
4.4.1 Exact
4.4.2 Approximate
Prerequisiti
Basic knowledge on: probability theory, calculus, statistics, mathematics.
Good skills to design and develop computer programs.
Modalità didattica
Teaching is achieved by classes and hands-on lectures (using Python).
The material is organized through learning paths where lecture modules consist of theoretical lecture and hands-on lecture.
Materiale didattico
- Introdution to Data Mining (https://www-users.cse.umn.edu/~kumar001/dmbook/index.php)
- Bayesian Networks and Decision Graphs (https://link.springer.com/book/10.1007/978-0-387-68282-2)
Periodo di erogazione dell'insegnamento
Spring Semester
Modalità di verifica del profitto e valutazione
Assessment is based on two components, a project, the lab reports and an interview on the methodological contents of the course.
Students are suggested to work in pairs during the lab activity.
Students are encouraged to work in small teams to design, develop and document their unsupervised learning project.
Orario di ricevimento
To be agreed on by mail message.
Aims
To develop skills for solving real worls unsupervised learning problems.
The goal is achieved by;
- Teaching how to design, train, deploy and monitor unsupervised learning models.
- Exploiting open source platforms, languages and software,
- Stimulating team working.
Contents
The course contents are the following;
- Data Types; to list different types of data and to learn hw they must be used for unsupervised learning.
- Data Preprocessing; to preprocess data in such a way it can be used by unsupervised learning tasks,
- Clustering Learning; to form homogeneous groups of observations and/or attributes using a given proximity measure,
- Clustering Validation; to evaluate and compare diferent clusteirng solutions to select the one to deploy.
- Anomaly Detection; to find anomalous observations, to discover outliers observations, under different theoretical settings.
- Bayesian Networks; to learn probabilistic/causal structure from data and to make decisions under uncertainty.
You will learn how to design, train, validate and deploy unsupervised learning models using different programming languages; mainly Python and R.
Detailed program
1. Data
1.1 Data types and attributes
1.2 Proximity measures for nominal, ordinal and continuous attributes
1.3 Data Pre-Processing
2. Cluster Analysis
2.1 Introduction
2.2 Clustering algorithms
2.2.1 Partitioning
2.2.2 Hierarchical
2.2.3 Graph-based
2.2.4 Density-based
2.2.5 Time-series
2.3 Comparing clustering solutions
2.3.1 Performance measures
2.3.2 Evaluation
2.3.3 Comparison
3. Anomaly Detection
3.1 Introduction
3.2 Anomaly detection algorithms
3.2.1 Statistical approaches
3.2.2 Proximity-based approaches
3.2.3 Clustering-based approaches
3.2.4 One-class classification
3.2.5 Information theoretic approaches
4. Bayesian Networks
4.1 Introduction
4.2 Bayesian network models
4.2.1 Discrete variables
4.2.2 Continuous variables
4.2.3 Mixed variables
4.3 Learning
4.3.1 Parameters
4.3.2 Structure
4.4 Inference
4.4.1 Exact
4.4.2 Approximate
Prerequisites
Basic knowledge on: probability theory, calculus, statistics, mathematics.
Good skills to design and develop computer programs.
Teaching form
Teaching is achieved by classes and hands-on lectures (using Python).
The material is organized through learning paths where lecture modules consist of theoretical lecture and hands-on lecture.
Textbook and teaching resource
- Introdution to Data Mining (https://www-users.cse.umn.edu/~kumar001/dmbook/index.php)
- Bayesian Networks and Decision Graphs (https://link.springer.com/book/10.1007/978-0-387-68282-2)
Semester
Spring Semester
Assessment method
Assessment is based on three components, a project, the lab reports and an interview on the methodological contents of the course.
Students are suggested to work in pairs during the lab activity.
Students are encouraged to work in small teams to design, develop and document their unsupervised learning project.
Office hours
To be agreed on by mail message.
Staff
-
Giulia Cisotto
-
Fabio Antonio Stella