Course 2021-2022 a.y.

30557 - AI LAB

Department of Decision Sciences

Course taught in English
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


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 bio-informatics
  • Bio-informatics batabases
  • Individual projects: application of machine learning to real 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...

At the end of the course students will be able to undesrtand how to:

  •  Handle complex databases
  • Apply different types of algorithms for data analysis: unsupervised clustering, supervised predictions, dimensional reduction.
  • Evaluate performance based on domain knowledge and rigorous tests.

 

APPLYING KNOWLEDGE AND UNDERSTANDING

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

At the end of the course students 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.)
  • Individual assignments

DETAILS

  • Exercises: implement different algoritms for data analysis, using machine learning software and extracting infomation  from datasets of real world interest.
  • Individual assignement: solve a real prediction problem

Assessment methods

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

ATTENDING AND NOT ATTENDING STUDENTS

The assesment will be based on the outcome of the individual and the group projects.

A written report will be required for each student.

One student for each group project will have give an  oral presentation in lcass.

 

Grading scheme:

Individual project: 50%

Goup project: 50%


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

The teachning material will be fully provided by the teacher at the beginning of the course.

Last change 29/06/2021 00:30