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Data Science and Artificial Intelligence

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  • Updated on
  • 24 Apr 2023
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The new master program in Data Science and Artificial Intelligence (class LM data), starting from September 2023,  is an international master program, taught in English, focussed on foundational and state-of-the-art techniques in Data Science, Machine Learning, Artificial Intelligence, and their application to solve problems in Industry, Medicine, Business.

The new master program in Data Science and Artificial Intelligence is organised with the participation of SISSA (International School for Advanced Studies), ICTP (International Centre for Theoretical Physics), Area Science Park (Scientific and Technologic park), and INAF (National Institute of Astrophysics). It also features the collaboration and support of some companies.

The program is organised in four curricula:

Admission Requirements

  • academic degree, or equivalent, with a graduation grade greater or equal to 80 over 110. In the absence of a Bachelor Degree, the weighted average of the exams will be considered instead.
  • obtained at least 30 ECTS in total in the following topics:
    • mathematics (MAT/*)
    • computer science (INF/01)
    • information engineering (ING-INF/*)
    • physics (FIS/*)
    • statistics and mathematical methods for decisions (SECS-S/*)
    • quantitative economy and finance (SECS-P01, 05)
    • molecular biology and genetics (BIO/10, 11, 18)
  • among the 30 ECTS above, at least 9 ECTS must be obtained in mathematics (MAT/*, SECS-S/06)
  • adequate knowledge of English, certified by internationally recognized certification of level B2 or higher, or by passing a test equivalent to level B2.

 

Curricula 

Here we will provide a brief overview of the four curricula. More details will be available soon in a dedicated website. 

All curricula share a common set of core courses, giving the necessary knowledge in the areas of computer science, statistics and modelling, machine learning. Each curriculum has some specific core course and electives. For some courses, students have to choose between two options: typically, one course provides also introductory material (marked with * below) - for students who never studied the subject - while the other one treats more advanced topics, assuming a basic knowledge of the subject - for students that had already courses on that specific subject. Each student will have to choose one of the two courses. This will permit to assemble a personalised study plan depending on the background. 

 

Foundations of Artificial Intelligence and Machine Learning

I Year
Advanced programming (*) or Machine Learning Operations  (6 ECTS)
High Performance and Cloud Computing (9 ECTS)
Statistical Methods (9 ECTS)
Algorithmic Design (*) or Algorithmic Data Mining (6 ECTS)
Introduction to Machine Learning (*) or Optimization for Artificial Intelligence (6 ECTS)
Probabilistic and Deep Machine Learning (12 ECTS)
Reinforcement Learning (6 ECTS)
Ethics and Law of Data and Artificial Intelligence (6 ECTS)

II Year
Core Elective Course (group A) (6 ECTS)
Core Elective Course (group B) or  Optimization for Artificial Intelligence (if not taken in the first year) (6 ECTS)
Core Elective Course (group B) or  Data Management (*) (6 ECTS)
Two Elective Courses (12 ECTS)
Internship (12 ECTS)
Thesis (18 ECTS)

 

Core Electives - group A (Artificial Intelligence) - each course has 6 ECTS
Introduction to Artificial Intelligence (*)
Symbolic and Neuro-Symbolic Artificial Intelligence
Explainable and Reliable Artificial Intelligence
Multi-Agent Systems
Simulation Intelligence and Learning for Autonomous Systems

Core Electives - group B (Machine Learning and Data) - each course has 6 ECTS
Unsupervised Learning
Computer Vision and Pattern Recognition
Advanced Deep Learning and Kernel Methods
Natural Language Processing
Advanced Statistical Methods
Information Retrieval and Data Visualisation
Advanced Data Management

Electives  - each course has 6 ECTS
All core electives not taken before
Information Theory
Data Management
Stochastic Modelling and Simulation
Mathematical Optimisation
Bayesian Statistics
Machine Learning Operations
Advanced Topics in Machine Learning
Software Development Methods
Advanced High Performance Computing
High Performance Computing and Data Infrastructures
Artificial Intelligence for Cyber-Physical Systems

 

 

Data Science and Artificial Intelligence for Industry and Cyber-Physical System

I Year
Advanced programming (*) or Machine Learning Operations  (6 ECTS)
High Performance and Cloud Computing (9 ECTS)
Statistical Methods (9 ECTS)
Introduction to Machine Learning (*) or Optimization for Artificial Intelligence or Mathematical Optimization (6 ECTS)
Probabilistic and Deep Machine Learning (12 ECTS)
Reinforcement Learning (6 ECTS)
Modelling and Control of Cyber-Physical Systems I (6 ECTS)
Ethics and Law of Data and Artificial Intelligence or Numerical Analysis (*) (6 ECTS)

II Year
Modelling and Control of Cyber-Physical Systems II (6 ECTS)
Core Elective Course or  Optimization for Artificial Intelligence or Mathematical Optimization (in case no optimization course has been taken in first year) (6 ECTS)
Core Elective Course or  Data Management (*) (6 ECTS)
Elective Course or Ethics and Law of Data and Artificial Intelligence (if not taken in first year) (6 ECTS)
Elective Course (6 ECTS)
Internship (12 ECTS)
Thesis (18 ECTS)

 

Core Electives - each course has 6 ECTS
Artificial Intelligence for Cyber-Physical Systems
Simulation Intelligence and Learning for Autonomous Systems

Electives  - each course has 6 ECTS
All core electives or core courses not taken before
Stochastic Modelling and Simulation
Information Theory
Unsupervised Learning
Computer Vision and Pattern Recognition
Advanced Deep Learning and Kernel Methods
Advanced Topics in Machine Learning
Natural Language Processing
Symbolic and Neuro-Symbolic Artificial Intelligence
Explainable and Reliable Artificial Intelligence
Introduction to Artificial Intelligence
Mathematical Optimisation
Bayesian Statistics
Advanced Probability
Advanced Data Management
Software Development Methods
Machine Learning Operations
Advanced High Performance Computing
GPU and Parallel Programming
Information Retrieval and Data Visualisation
Advanced Statistical Methods
High Performance Computing and Data Infrastructures

 

 

 

Data Science and Artificial Intelligence for Health and Life Sciences

I Year
Advanced programming (*) or Machine Learning Operations  (6 ECTS)
High Performance and Cloud Computing (9 ECTS)
Statistical Methods (9 ECTS)
Algorithmic Design (*) or Algorithmic Data Mining (6 ECTS)
Introduction to Machine Learning (*) or Unsupervised Learning (6 ECTS)
Probabilistic and Deep Machine Learning (12 ECTS)
Statistical Learning in Epidemiology (6 ECTS)
Data Management  (*) or Stochastic Modelling and Simulation or Advanced Statistical Methods (6 ECTS)

II Year
Computational Genomics (6 ECTS)
Core Elective Course or  Unsupervised Learning (if not taken in first year) (6 ECTS)
Ethics and Law of Data and Artificial Intelligence (6 ECTS)
Two Elective Courses (12 ECTS)
Internship (12 ECTS)
Thesis (18 ECTS)

 

Core Electives - each course has 6 ECTS
Stochastic Modelling and Simulation
Computational Neuroscience
Advanced Statistical Methods

 

Electives  - each course has 6 ECTS
All core electives or core courses not taken before
Molecular Biology
Bioinformatics (3 ECTS)
Management of Health Data
Molecular Simulation
Information Retrieval and Data Visualisation
Information Theory
Advanced Deep Learning and Kernel Methods
Computer Vision and Pattern Recognition
Natural Language Processing
Mathematical Optimisation
Bayesian Statistics
Advanced Data Management
Software Development Methods
Machine Learning Operations
Multi-Agent Systems
Simulation Intelligence and Learning for Autonomous Systems
Symbolic and Neuro-Symbolic Artificial Intelligence
Explainable and Reliable Artificial Intelligence
Advanced High Performance Computing
High Performance Computing and Data Infrastructures

 

 

 

Data Science and Artificial Intelligence for Economy and Society

I Year
Advanced programming (*) or Machine Learning Operations  (6 ECTS)
High Performance and Cloud Computing (9 ECTS)
Statistical Methods (9 ECTS)
Introduction to Machine Learning (*) or Unsupervised Learning (6 ECTS)
Probabilistic and Deep Machine Learning (12 ECTS)
Advanced Statistical Methods (6 ECTS)
Data Management (*) or Data Science for Insurance or Bayesian Statistics (6 ECTS)
Ethics and Law of Data and Artificial Intelligence or Entrepreneurship and Business Modelling (6 ECTS)

II Year
Three Core Elective Courses (18 ECTS)
Two Elective Courses (6 ECTS)
Internship (12 ECTS)
Thesis (18 ECTS)

 

Core Electives - each course has 6 ECTSStatistical Analysis of Social Networks
Data Science for Insurance
Natural Language Processing
Bayesian Statistics
Multi-Agent Systems 
Unsupervised Learning
Information Retrieval and Data Visualisation

Electives  - each course has 6 ECTS
All core electives or core courses not taken before
Business Analytics
Quantitative Finance
Computer Vision and Pattern Recognition
Advanced Topics in Machine Learning
Explainable and Reliable Artificial Intelligence
Mathematical Optimisation
Advanced Data Management
Software Development Methods
Machine Learning Operations
Time-series analysis
Symbolic and Neuro-Symbolic Artificial Intelligence
Stochastic Modelling and Simulation
Simulation Intelligence and Learning for Autonomous Systems
High Performance Computing and Data Infrastructures