30422 - TECHNOLOGICAL INNOVATION SEMINARS II

Data Science, Machine Learning and AI are highly attractive disciplines nowadays. This course aims at providing a clear overview of the differences, similarities and synergies among such disciplines. This general overview is then specialized to the field of Network Science through a number of statistical and algorithmic models which clarify how Data Science, Machine Learning and AI can substantially expand knowledge on the structure and function of the complex connectivity data routinely collected in different fields, such as political science, neuroscience and criminology.

• A general overview of Data Science, Machine Learning and AI (differences, similarities and synergies)
• Statistical and algorithmic models in Network Science (force-directed placement algorithms, community detection algorithms, stochastic block models, latent space models)

• Explain differences, similarities and synergies among Data Science, Machine Learning and AI disciplines
• Distinguish between model-based and algorithmic-based techniques for the analysis of network data
• Describe the main properties and implementation details of model-based and algorithmic-based techniques for the analysis of network data
• Identify the most suitable technique to analyse a given network according to a specific research objective

• Connect and compare Data Science, Machine Learning and AI disciplines
• Apply standard statistical softwares for the analysis of network data
• Develop codes to implement specific model-based and algorithmic-based analyses of networks
• Discuss the ouput of relevant statistical models for network data 

• Face-to-face lectures
• Case studies /Incidents (traditional, online)

Each method presented is directly motivated and illustrated on a number of relevant case studies from political sciences, neurosciences and criminology. These case studies showcase the potentials of Data Science, Machine Learning and AI in the specific field of Network Science.

Full credit is assigned automatically to all students who meet the active class participation condition.

For students who do not meet the active class participation condition, the full credit is assigned after a successful oral individual exam on the topics presented during the course. 

In particular, the oral individual exam will be based on questions assessing the ability of students to explain differences, similarities and synergies among Data Science, Machine Learning and AI, while connecting and comparing such disciplines. Additional questions will also evaluate whether students can distinguish between model-based and algorithmic-based techniques for the analysis of network data, while describing the main properties, implementation details and outputs of these techniques.

The course is entirely based on slides and research articles. All slides and research articles are made available to both attending and non-attending students.