Master's Degree

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE

The course Study plan Syllabi

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Curriculum

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR ECONOMY AND SOCIETY

Year:

1

2

Required:

HIGH PERFORMANCE AND CLOUD COMPUTING [297SM]

9 cfu

72 hours

ADVANCED STATISTICAL METHODS [332SM]

6 cfu

48 hours

ETHICS AND LAW OF DATA AND ARTIFICIAL INTELLIGENCE [341SM]

6 cfu

48 hours

PROBABILISTIC AND DEEP MACHINE LEARNING [342SM]

12 cfu

96 hours

STATISTICAL METHODS [344SM]

9 cfu

72 hours

UNSUPERVISED LEARNING [362SM]

6 cfu

48 hours

1 optional course between (6 CFU):

MACHINE LEARNING OPERATIONS [305SM]

6 cfu

48 hours

ADVANCED PROGRAMMING [467SM]

6 cfu

48 hours

1 optional course between (6 CFU):

OPTIMIZATION FOR ARTIFICIAL INTELLIGENCE [343SM]

6 cfu

48 hours

INTRODUCTION TO MACHINE LEARNING [470SM]

6 cfu

48 hours

Required:

INTERNSHIP [857SM]

12 cfu

0 hours

FINAL EXAMINATION [PFINE]

18 cfu

0 hours

1 optional course between (6 CFU):

ENTREPRENEURSHIP AND BUSINESS MODELLING [325SM]

6 cfu

48 hours

MULTI-AGENT SYSTEMS [335SM]

6 cfu

48 hours

DATA MANAGEMENT [339SM]

6 cfu

60 hours

DATA SCIENCE FOR INSURANCE [372SM]

6 cfu

48 hours

STATISTICAL ANALYSIS OF NETWORKS [373SM]

6 cfu

48 hours

NATURAL LANGUAGE PROCESSING [478SM]

6 cfu

48 hours

INFORMATION RETRIEVAL AND DATA VISUALIZATION [481SM]

6 cfu

48 hours

BAYESIAN STATISTICS [553SM]

6 cfu

48 hours

RESPONSIBLE AND FAIR ARTIFICIAL INTELLIGENCE [985MG]

6 cfu

48 hours

EXPLAINABLE, CAUSAL AND NEURO-SYMBOLIC ARTIFICIAL INTELLIGENCE [986MG]

6 cfu

48 hours

2 optional courses between (12 CFU):

ENTREPRENEURSHIP AND BUSINESS MODELLING [325SM]

6 cfu

48 hours

MULTI-AGENT SYSTEMS [335SM]

6 cfu

48 hours

DATA SCIENCE FOR INSURANCE [372SM]

6 cfu

48 hours

STATISTICAL ANALYSIS OF NETWORKS [373SM]

6 cfu

48 hours

NATURAL LANGUAGE PROCESSING [478SM]

6 cfu

48 hours

INFORMATION RETRIEVAL AND DATA VISUALIZATION [481SM]

6 cfu

48 hours

BAYESIAN STATISTICS [553SM]

6 cfu

48 hours

RESPONSIBLE AND FAIR ARTIFICIAL INTELLIGENCE [985MG]

6 cfu

48 hours

EXPLAINABLE, CAUSAL AND NEURO-SYMBOLIC ARTIFICIAL INTELLIGENCE [986MG]

6 cfu

48 hours

Elective courses (12 CFU):
Curriculum

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR HEALTH AND LIFE SCIENCES

Year:

1

2

Required:

HIGH PERFORMANCE AND CLOUD COMPUTING [297SM]

9 cfu

72 hours

ETHICS AND LAW OF DATA AND ARTIFICIAL INTELLIGENCE [341SM]

6 cfu

48 hours

PROBABILISTIC AND DEEP MACHINE LEARNING [342SM]

12 cfu

96 hours

STATISTICAL METHODS [344SM]

9 cfu

72 hours

1 optional course between (6 CFU):

MACHINE LEARNING OPERATIONS [305SM]

6 cfu

48 hours

ADVANCED PROGRAMMING [467SM]

6 cfu

48 hours

1 optional course between (6 CFU):

UNSUPERVISED LEARNING [362SM]

6 cfu

48 hours

INTRODUCTION TO MACHINE LEARNING [470SM]

6 cfu

48 hours

1 optional course between (6 CFU):

ALGORITHMIC DESIGN [298SM]

6 cfu

48 hours

ALGORITHMIC DATA MINING [340SM]

6 cfu

48 hours

1 optional course between (6 CFU):

STATISTICAL LEARNING IN EPIDEMIOLOGY [330SM]

6 cfu

48 hours

STOCHASTIC MODELLING AND SIMULATION [580SM]

6 cfu

48 hours

Required:

COMPUTATIONAL GENOMICS [327SM]

6 cfu

48 hours

INTERNSHIP [857SM]

12 cfu

0 hours

FINAL EXAMINATION [PFINE]

18 cfu

0 hours

Optional course (6 CFU):

UNSUPERVISED LEARNING [362SM]

6 cfu

48 hours

1 optional course between (6 CFU):

COMPUTATIONAL NEUROSCIENCE [326SM]

6 cfu

48 hours

STATISTICAL LEARNING IN EPIDEMIOLOGY [330SM]

6 cfu

48 hours

ADVANCED STATISTICAL METHODS [332SM]

6 cfu

48 hours

STOCHASTIC MODELLING AND SIMULATION [580SM]

6 cfu

48 hours

1 optional course between (6 CFU):

COMPUTATIONAL NEUROSCIENCE [326SM]

6 cfu

48 hours

STATISTICAL LEARNING IN EPIDEMIOLOGY [330SM]

6 cfu

48 hours

ADVANCED STATISTICAL METHODS [332SM]

6 cfu

48 hours

DATA MANAGEMENT [339SM]

6 cfu

48 hours

STOCHASTIC MODELLING AND SIMULATION [580SM]

6 cfu

48 hours

Elective courses (12 CFU):
Curriculum

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR INDUSTRY AND DIGITAL TWINS

Year:

1

2

Required:

HIGH PERFORMANCE AND CLOUD COMPUTING [297SM]

9 cfu

72 hours

ETHICS AND LAW OF DATA AND ARTIFICIAL INTELLIGENCE [341SM]

6 cfu

48 hours

PROBABILISTIC AND DEEP MACHINE LEARNING [342SM]

12 cfu

96 hours

STATISTICAL METHODS [344SM]

9 cfu

72 hours

STOCHASTIC MODELLING AND SIMULATION [580SM]

6 cfu

48 hours

1 optional course between (6 CFU):

MACHINE LEARNING OPERATIONS [305SM]

6 cfu

48 hours

ADVANCED PROGRAMMING [467SM]

6 cfu

48 hours

1 optional course between (6 CFU):

OPTIMIZATION FOR ARTIFICIAL INTELLIGENCE [343SM]

6 cfu

48 hours

INTRODUCTION TO MACHINE LEARNING [470SM]

6 cfu

48 hours

1 optional course between (6 CFU):

ADVANCED NUMERICAL ANALYSIS [579SM]

6 cfu

48 hours

NUMERICAL ANALYSIS [584SM]

6 cfu

48 hours

NUMERICAL METHODS FOR MACHINE LEARNING [983MG]

6 cfu

48 hours

Required:

INTERNSHIP [857SM]

12 cfu

0 hours

SCIENTIFIC MACHINE LEARNING AND SIMULATION INTELLIGENCE [984MG]

6 cfu

48 hours

SAFE AND VERIFIED ARTIFICIAL INTELLIGENCE [998MG]

6 cfu

48 hours

FINAL EXAMINATION [PFINE]

18 cfu

0 hours

1 optional course between (6 CFU):

MATHEMATICAL OPTIMISATION [446SM]

6 cfu

48 hours

REINFORCEMENT LEARNING [453SM]

6 cfu

48 hours

EXPLAINABLE, CAUSAL AND NEURO-SYMBOLIC ARTIFICIAL INTELLIGENCE [986MG]

6 cfu

48 hours

CONTROL OF CYBER-PHYSICAL SYSTEMS [987MG]

6 cfu

48 hours

Elective courses (12 CFU):
Curriculum

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Year:

1

2

Required:

HIGH PERFORMANCE AND CLOUD COMPUTING [297SM]

9 cfu

72 hours

ETHICS AND LAW OF DATA AND ARTIFICIAL INTELLIGENCE [341SM]

6 cfu

48 hours

PROBABILISTIC AND DEEP MACHINE LEARNING [342SM]

12 cfu

96 hours

STATISTICAL METHODS [344SM]

9 cfu

72 hours

REINFORCEMENT LEARNING [453SM]

6 cfu

48 hours

1 optional course between (6 CFU):

MACHINE LEARNING OPERATIONS [305SM]

6 cfu

48 hours

ADVANCED PROGRAMMING [467SM]

6 cfu

48 hours

1 optional course between (6 CFU):

OPTIMIZATION FOR ARTIFICIAL INTELLIGENCE [343SM]

6 cfu

48 hours

INTRODUCTION TO MACHINE LEARNING [470SM]

6 cfu

48 hours

1 optional course between (6 CFU):

ALGORITHMIC DESIGN [298SM]

6 cfu

48 hours

ALGORITHMIC DATA MINING [340SM]

6 cfu

48 hours

Required:

INTERNSHIP [857SM]

12 cfu

0 hours

FINAL EXAMINATION [PFINE]

18 cfu

0 hours

Optional course (6 CFU):

OPTIMIZATION FOR ARTIFICIAL INTELLIGENCE [343SM]

6 cfu

48 hours

1 optional course between (6 CFU):

ADVANCED DATA MANAGEMENT [328SM]

6 cfu

48 hours

ADVANCED STATISTICAL METHODS [332SM]

6 cfu

48 hours

ADVANCED DEEP LEARNING AND KERNEL METHODS [333SM]

6 cfu

48 hours

MULTI-AGENT SYSTEMS [335SM]

6 cfu

48 hours

UNSUPERVISED LEARNING [362SM]

6 cfu

48 hours

NATURAL LANGUAGE PROCESSING [478SM]

6 cfu

48 hours

INFORMATION RETRIEVAL AND DATA VISUALIZATION [481SM]

6 cfu

48 hours

COMPUTER VISION AND PATTERN RECOGNITION [554SM]

6 cfu

48 hours

SCIENTIFIC MACHINE LEARNING AND SIMULATION INTELLIGENCE [984MG]

6 cfu

48 hours

RESPONSIBLE AND FAIR ARTIFICIAL INTELLIGENCE [985MG]

6 cfu

48 hours

EXPLAINABLE, CAUSAL AND NEURO-SYMBOLIC ARTIFICIAL INTELLIGENCE [986MG]

6 cfu

48 hours

SAFE AND VERIFIED ARTIFICIAL INTELLIGENCE [998MG]

6 cfu

48 hours

1 optional course between (6 CFU):

ADVANCED DATA MANAGEMENT [328SM]

6 cfu

48 hours

ADVANCED STATISTICAL METHODS [332SM]

6 cfu

48 hours

ADVANCED DEEP LEARNING AND KERNEL METHODS [333SM]

6 cfu

48 hours

DATA MANAGEMENT [339SM]

6 cfu

60 hours

UNSUPERVISED LEARNING [362SM]

6 cfu

48 hours

NATURAL LANGUAGE PROCESSING [478SM]

6 cfu

48 hours

INFORMATION RETRIEVAL AND DATA VISUALIZATION [481SM]

6 cfu

48 hours

COMPUTER VISION AND PATTERN RECOGNITION [554SM]

6 cfu

48 hours

SCIENTIFIC MACHINE LEARNING AND SIMULATION INTELLIGENCE [984MG]

6 cfu

48 hours

1 optional course between (6 CFU):

MULTI-AGENT SYSTEMS [335SM]

6 cfu

48 hours

RESPONSIBLE AND FAIR ARTIFICIAL INTELLIGENCE [985MG]

6 cfu

48 hours

EXPLAINABLE, CAUSAL AND NEURO-SYMBOLIC ARTIFICIAL INTELLIGENCE [986MG]

6 cfu

48 hours

SAFE AND VERIFIED ARTIFICIAL INTELLIGENCE [998MG]

6 cfu

48 hours

Elective courses (12 CFU):
Curriculum

HIGH PERFORMANCE COMPUTING FOR DATA SCIENCE AND ARTIFICIAL INTELLIGENCE

Year:

1

2

Required:

HIGH PERFORMANCE AND CLOUD COMPUTING [297SM]

9 cfu

72 hours

ETHICS AND LAW OF DATA AND ARTIFICIAL INTELLIGENCE [341SM]

6 cfu

48 hours

PROBABILISTIC AND DEEP MACHINE LEARNING [342SM]

12 cfu

96 hours

STATISTICAL METHODS [344SM]

9 cfu

72 hours

ADVANCED PROGRAMMING [467SM]

6 cfu

48 hours

1 optional course between (6 CFU):

MACHINE LEARNING OPERATIONS [305SM]

6 cfu

48 hours

INTRODUCTION TO MACHINE LEARNING [470SM]

6 cfu

48 hours

1 optional course between (6 CFU):

ALGORITHMIC DESIGN [298SM]

6 cfu

48 hours

ALGORITHMIC DATA MINING [340SM]

6 cfu

48 hours

1 optional course between (6 CFU):

ADVANCED DATA MANAGEMENT [328SM]

6 cfu

48 hours

DATA MANAGEMENT [339SM]

6 cfu

60 hours

Required:

HIGH PERFORMANCE COMPUTING AND DATA INFRASTRUCTURES [306SM]

6 cfu

48 hours

MATHEMATICAL OPTIMISATION [446SM]

6 cfu

48 hours

INTERNSHIP [857SM]

12 cfu

0 hours

FINAL EXAMINATION [PFINE]

18 cfu

0 hours

1 optional course between (6 CFU):

ADVANCED HIGH PERFORMANCE COMPUTING [307SM]

6 cfu

48 hours

ADVANCED DATA MANAGEMENT [328SM]

6 cfu

48 hours

Optional course (6 CFU):

ADVANCED HIGH PERFORMANCE COMPUTING [307SM]

6 cfu

48 hours

Elective courses (12 CFU):
Curriculum

comune

Year:

Other activities

PREPARATORY COURSE [483SM]

3 cfu

24 hours

[SM38] Study Plan 2025/26 | ITA only

[SM38] Study Plan 2024/25 | ITA only

[SM38] Study Plan 2023/24 | ITA only

Teaching Regulations

For Study Plans and Teaching Regulation of previous cohorts/years.

see teaching regulations

Study Plan description

Types of educational activities (TAF), credits and elective courses

see Study Plan description

Double Degree

see double degree page

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DATA SCIENCE AND ARTIFICIAL INTELLIGENCE

Study plan

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR ECONOMY AND SOCIETY

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR HEALTH AND LIFE SCIENCES

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR INDUSTRY AND DIGITAL TWINS

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

HIGH PERFORMANCE COMPUTING FOR DATA SCIENCE AND ARTIFICIAL INTELLIGENCE

comune

Department

Location

Ministerial degree class

Double degree

Inter-university

Admission

Teaching modality

PA 110 cum laude

Visa applicants

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE

Main content

Study plan

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR ECONOMY AND SOCIETY

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR HEALTH AND LIFE SCIENCES

DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR INDUSTRY AND DIGITAL TWINS

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

HIGH PERFORMANCE COMPUTING FOR DATA SCIENCE AND ARTIFICIAL INTELLIGENCE

comune

Department

Location

Ministerial degree class

Double degree

Inter-university

Admission

Teaching modality

PA 110 cum laude

Visa applicants