Course 2024-2025 a.y.

30557 - AI LAB

Department of Computing Sciences

Course taught in English
BAI (1 credits - II sem. - OB)
Course Director:
FRANCESCA BUFFA

Classi: 27 (I/II sem.)
Docenti responsabili delle classi:
Classe 27: FRANCESCA BUFFA


Conoscenze pregresse consigliate

For a fruitful and effective learning experience, it is recommended a preliminary knowledge of basic linear algebra, elements of probability and statistics, calculus, optimization and programming (Python)

Mission e Programma sintetico

MISSION

The purpose of the lab will be to apply basic machine learning techniques to real data. Students will be expected to tackle problems of bio-medical interest and learn how to extract relevant information from complex data. The projects will be preceded by an introduction to biomedical models, in order to be able to critically evaluate the results obtained.

PROGRAMMA SINTETICO

  • Elements of informatics for life sciences
  • Biomedical and life sciences databases
  • Individual projects: application of machine learning to real life science problems with a critical assessment of the results.

Risultati di Apprendimento Attesi (RAA)

CONOSCENZA E COMPRENSIONE

Al termine dell'insegnamento, lo studente sarà in grado di...
  • Handle complex databases
  • Apply different types of algorithms for data analysis: unsupervised clustering, dimensional reduction, supervised predictions.
  • Evaluate performance based on domain knowledge and rigorous tests.

 

CAPACITA' DI APPLICARE CONOSCENZA E COMPRENSIONE

Al termine dell'insegnamento, lo studente sarà in grado di...

- approach the solution to data analysis problems coming from a real world context,

- use fundamental machine learning  algorithms.

- critically evaluate the results


Modalità didattiche

  • Lezioni frontali
  • Esercitazioni (esercizi, banche dati, software etc.)

DETTAGLI

  • Lectures and exercises: Concepts in data analysis using machine learning to extract infomation  from datasets of real world interest. The necessary domain specific knowledge will be provided.
  • Group assignement: solve a real prediction problem
  • Presentation of project as a group

Metodi di valutazione dell'apprendimento

  Accertamento in itinere Prove parziali Prova generale
  • Prova individuale orale
     x
  • Assignment individuale (relazione, esercizio, dimostrazione, progetto etc.)
     x
  • Assignment di gruppo (relazione, esercizio, dimostrazione, progetto etc.)
     x

STUDENTI FREQUENTANTI E NON FREQUENTANTI

A group project will be assigned to the students to verify they are able to:

  • approach the solution to data analysis problems coming from a real world context in the best way
  • use fundamental machine learning  algorithms, selecting the best ones for the problem at hand
  • handle complex databases
  • critically evaluate the results based on domain knowledge and rigorous tests

 

 

The assesment will be based on the outcome of the group projects (50%) and on the contribution of each student to the project (50%).

 

For the group: the group must deliver a written final report and give an oral presentation together at the exam session. For each student: each student will be asked to present their part in detail and discuss their contribution to the project and to the report.


Materiali didattici


STUDENTI FREQUENTANTI E NON FREQUENTANTI

All data, instructions and study material will be provided during the course

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