The goal of this course is to provide an overview of Machine Learning algorithms dealing with sequential/dynamic data and agents that can interact with the environment within the reinforcement learning framework.
Learning how to use sequential and reinforcement learning algorithms by grasping the underlying computational and modeling issues.
At the end of the course, students will be able to:
UNDERSTAND and use machine learning algorithms and models for dynamic data and agents
UNDERSTAND how to effectively set-up machine learning pipelines with dynamic data/agents
IMPLEMENT the learning algorithms presented in the course
DEVELOP the ability to critically analyze analytical results
Basic probability, calculus, linear algebra, programming.
Theoretical classes might be complemented by practical lab sessions
The course will cover the following topics:
The material provided by the instructors (notes, papers, books), see the course Aulaweb page additional references.
Ricevimento: Appointment by email
ALESSANDRO VERRI (President)
NICOLETTA NOCETI
LORENZO ROSASCO (President Substitute)
According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/10852/studenti-orario
The timetable for this course is available here: EasyAcademy
The exam will be a project and a discussion of the material presented in the course.
Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/10852/studenti-disabilita-dsa
The exam will evaluate the overall understanding of course material, the capability to generalize the concepts to unseen problems and analyze the obtained results. Clarity of exposition, completeness of the concepts, quality of the proposed solutions and critical thinking will be taken into account.
For further information, please refer to the course’s AulaWeb module or contact the instructor.
