Insegnamento a.a. 2023-2024

30557 - AI LAB

Department of Computing Sciences

Course taught in English

Class timetable
Exam timetable
Go to class group/s: 27
BAI (1 credits - II sem. - OB)
Course Director:
FRANCESCA BUFFA

Classes: 27 (II sem.)
Instructors:
Class 27: FRANCESCA BUFFA


Synchronous Blended: Lezioni erogate in modalità sincrona in aula (max 1 ora per credito online sincrona)

Suggested background knowledge

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 & Content Summary

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.

CONTENT SUMMARY

  • 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.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • 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.

 

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

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

- use fundamental machine learning  algorithms.

- critically evaluate the results


Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)

DETAILS

  • 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

Assessment methods

  Continuous assessment Partial exams General exam
  • Oral individual exam
     x
  • Individual assignment (report, exercise, presentation, project work etc.)
     x
  • Group assignment (report, exercise, presentation, project work etc.)
     x

ATTENDING AND NOT ATTENDING STUDENTS

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.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

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

Last change 07/12/2023 20:00