MACHINE LEARNING

MACHINE LEARNING

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Last update 22/09/2021 09:02
Code
90498
ACADEMIC YEAR
2021/2022
CREDITS
9 credits during the 1st year of 10852 COMPUTER SCIENCE (LM-18) GENOVA

5 credits during the 2nd year of 8732 Electronic Engineering (LM-29) GENOVA

5 credits during the 2nd year of 10376 CHEMICAL AND PROCESSES ENGINEERING (LM-22) GENOVA

5 credits during the 2nd year of 10720 ENVIRONMENTAL ENGINEERING (LM-35) GENOVA

SCIENTIFIC DISCIPLINARY SECTOR
INF/01
LANGUAGE
English
TEACHING LOCATION
GENOVA (COMPUTER SCIENCE )
semester
1° Semester
Teaching materials

OVERVIEW

The goal of this course is to provide an overview of classical Machine Learning algorithms, discussing modeling and computational aspects.

AIMS AND CONTENT

LEARNING OUTCOMES

Learning how to use classical supervised and unsupervised machine learning algorithms by grasping the underlying computational and modeling issues.

AIMS AND LEARNING OUTCOMES

Students will be provided with basic ideas behind statistical learning and a number of prototypical supervised approaches, including, local methods, regularization networks, linear and non linear models. The Course also covers basic unsupervised problems such as clustering and dimensionality reduction. Special effort is devoted to discussing how to set up a reliable machine learning pipeline.

Students will be involved in project activities.

PREREQUISITES

Basic probability, calculus, linear algebra, programming.

Teaching methods

Classes and practical lab sessions. 

SYLLABUS/CONTENT

Course content

  • Machine Learning basics
  • Empirical risk minimization
  • Feature maps and kernels
  • Variable selection and dimensionality reduction
  • Clustering
  • Neural Networks

RECOMMENDED READING/BIBLIOGRAPHY

Material provided by the instructors (slides and papers), see the course Aulaweb page additional references.

TEACHERS AND EXAM BOARD

Ricevimento: Appointment by email (nicoletta.noceti@unige.it)

Exam Board

NICOLETTA NOCETI (President)

ELENA NICORA

LORENZO ROSASCO (President Substitute)

ALESSANDRO VERRI (Substitute)

LESSONS

Teaching methods

Classes and practical lab sessions. 

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

EXAMS

Exam description

  • 40% continuous assessment
  • 20% project (in groups)
  • 40% theory oral  

Assessment methods

  • timely delivery of assignments
  • active participation in class
  • final project on a method or use-case, and presentation of the obtained results in a seminar
  • oral exam