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Percorso della pagina
  1. Science
  2. Master Degree
  3. Artificial Intelligence for Science and Technology [F9103Q - F9102Q]
  4. Courses
  5. A.Y. 2023-2024
  6. 1st year
  1. Unsupervised Learning
  2. Summary
Unità didattica Course full name
Unsupervised Learning
Course ID number
2324-1-F9102Q031-F9102Q034M
Course summary SYLLABUS

Blocks

Back to Machine Learning for Modelling

Course Syllabus

  • Italiano ‎(it)‎
  • English ‎(en)‎
Export

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 Python.

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 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.

Export

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 Python.

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 two components, a project 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.

Enter

Key information

Field of research
ING-INF/05
ECTS
6
Term
Second semester
Activity type
Mandatory to be chosen
Course Length (Hours)
56
Degree Course Type
2-year Master Degreee
Language
English

Staff

    Teacher

  • Giulia Cisotto
    Giulia Cisotto
  • Fabio Antonio Stella
    Fabio Antonio Stella

Enrolment methods

Manual enrolments
Self enrolment (Student)

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