Course 2024-2025 a.y.

20880 - MACHINE LEARNING LAB

Department of Computing Sciences

Course taught in English

Class timetable
Exam timetable
AI (2 credits - II sem. - OB  |  INF/01)
Course Director:
FRANCESCA BUFFA

Classi: 29 (I/II sem.)
Docenti responsabili delle classi:
Classe 29: FRANCESCA BUFFA


Conoscenze pregresse consigliate

For a fruitful and effective learning experience, it is recommended 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 fundamental problems in the life sciences and learn how to extract relevant information from complex data. The projects will be preceded by an introduction of the relevant domain knowledge needed to be able to critically evaluate the results obtained.

PROGRAMMA SINTETICO

Introduction to the use of AI in the Life Sciences: Basics concepts, Open questions, Methods, Challenges

Lab work: the students will be asked to address an open question in the life sciences, and will be given access to a series of datasets to address this question. The student will learn how to explore the data, evaluate applicability and apply advanced ML methods, focusing on methods studied in the course associated with the lab, and write a final report discussing their choices and 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 unsupervised and supervised data analysis: from basic algorithms to deep models
  • 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, including deep learning.
  • critically evaluate the results

Modalità didattiche

  • Lezioni frontali
  • Lavori/Assignment di gruppo
  • Altre attivita' d'aula interattive on campus/online (role playing, business game, simulation, online forum, instant polls)

DETTAGLI

  • Lectures and exercises: Concepts in data analysis using machine learning and deep 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 scritta (tradizionale/online)
    x
  • 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

The assesment will be based on the outcome of the group projects. A written report will be required for the group. The group members will have to give an oral presentation together at the exam session. Individual assessmet: each student will be asked to present and discuss their contribution to the project and the report. Grading scheme: Group project: 50% Individual assessment: 50%

 

 

 


Materiali didattici


STUDENTI FREQUENTANTI E NON FREQUENTANTI

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

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