Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

A.A. 2026/2027 A.A. 2025/2026

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

1° Year

ModulesCreditsTAFSSD
6
B
INF/01
12
B
ING-INF/05
12
B
INF/01

2° Year  It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2025/2026
- 2nd year - AI & cloud - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer vision & deep learning - delivered in 2025/2026 and in 2026/2027
6
B
ING-INF/05
6
B
INF/01
6
B
INF/01
6
B
ING-INF/05
6
B
INF/01
6
B
ING-INF/05
Between the years: 1°- 2°
2 courses among (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2025/2026
- 2nd year - AI & cloud, Visual intelligence - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer Vision & deep learning, Statistical learning - delivered in 2025/2026 and in 2026/2027
6
C
ING-INF/05
6
C
INF/01
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/06
Between the years: 1°- 2°
2 courses among the following
- A.A. 2025/2026 Network Science not activated
- A.A. 2026/2027: Complex Systems not activated
6
C
ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/08
6
C
MAT/09
6
C
INF/01 ,ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/06
6
C
INF/01
Between the years: 1°- 2°
1 course among the following
6
C
SECS-S/06
6
C
IUS/07
6
C
IUS/02
6
C
M-FIL/02
6
C
IUS/08
6
C
IUS/17
6
C
IUS/04
6
C
IUS/01
Between the years: 1°- 2°
Further activities: 3 CFU training and 3 CFU further language skill or 6 CFU training. International students (i.e. students who do not have an Italian bachelor’s degree) must compulsorily gain 3 CFU of Italian language skills (at least A2 level) and 3 CFU training.
6
F
-
Between the years: 1°- 2°
12
D
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Planning & Reinforcement Learning (2025/2026)

Teaching code

4S014024

Credits

12

Coordinator

Alberto Castellini

Language

English en

Also offered in courses:

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

Pianificazione en

Credits

6

Period

1st semester

Academic staff

Maria Paola Bonacina
Alessandro Farinelli

Lessons timetable
REINFORCEMENT LEARNING en

Credits

6

Period

2nd semester

Academic staff

Alberto Castellini

Lessons timetable

Learning objectives

The course is composed of two modules: Planning and Reinforcement Learning. The Planning module presents a selection of symbolic methods for planning. The students learn how to design, apply, and evaluate algorithms, procedures, and strategies for state space search and planning problems in different scenarios. At the end of the course the students know how to choose the most appropriate solution techniques for different
problems, and will be prepared to continue their studies in Artificial
Intelligence. The Reinforcement learning module introduces students to reinforcement learning and planning under uncertainty. In particular, it is focused on the design of algorithms that enable machines to learn based on reinforcements, hence from partial, implicit and delayed feedback obtained by repeatedly interact with the environment or users. At the end of the course, students will have to demonstrate that they have acquired the ability to i) tackle sequential decision problems with reinforcement learning techniques, ii) identify and apply the most effective and efficient algorithms to solve specific sequential decision problems, iii) designing new reinforcement learning algorithms. In particular, the acquired knowledge concerns advanced techniques for the resolution of Markov Decision Process (eg, research with Monte Carlo methods), bandit problems, model-based and model-free reinforcement learning, Bayesian reinforcement learning, deep reinforcement learning (DQN, Reinforce, A2C, PPO, DDPG, SAC), and advanced reinforcement learning techniques (safe policy improvement, partially observable environments, hierarchical reinforcement learning, imitation-based learning, inverse reinforcement learning, and meta-learning).

Prerequisites and basic notions

Being a first year, first/second semester exam, there are no specific prerequisites other than those required for access to the degree course.

Bibliography

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Criteria for the composition of the final grade

The final grade is represented by the arithmetic average of the grades of the two parts (Planning/RL) of the course.