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Master's Degree in Data Science
Data Mining Technology for Business and Society
Academic year 2021/202
Instructors:
Prof. Luca Becchetti: becchetti@ diag.uniroma1.it
Prof. Stefano Leonardi: leonardi@diag.uniroma1.it
Tutors:
Dr. Adriano Fazzone: fazzone@diag.uniroma1.it
Dr. Federico Siciliano: siciliano@diag.uniroma1.it
Zoom link: https://uniroma1.zoom.us/j/84935608829?pwd=NEROTTFIWGNYeXc3eXR3cmQ4ajJodz09
Enroll on the Google Classroom Site for Information and Material on the Class
Schedule:
Tuesday, 08:00 - 10:00, Room 15 - CU037, Piazzale Aldo Moro 5
Thursday, 15:00 - 19:00, Room 15 - CU037, Piazzale Aldo Moro 5
For meeting the instructors arrange an appointment by email
Program of the course:
The course will cover the following topics with lessons, laboratory and seminars from experts of specific applications.
Section I – Search Engine Technology
Boolean queries, Document Ranking and vectorial model
Evaluation of a Search Engine
Search Engine Technologies - Crawling
Locality - Sensitive Hashing e Duplicate detection
Link-analysis ranking
Section II – Recommender Systems
Content-based Recommendation
Collaborative Filtering
Matrix factorization
Dimensionality reduction
Singular Value Decomposition - Principal Component Analysis - LSI
Section III – Classification and Learning
Text Classification Unsupervised and Supervised
kNN, Naive Bayes, SVM
Introduction to NLP and Deep Learning
Applications: Fact checking and Misinformation
Exam:
There are two possibilities for the exam:
i) Deliver three homeworks assigned during the term and show good knowledge of the
topics of the course during an oral exam of discussion of the homeworks (Highly Recommended)
ii) Deliver a project assigned by the instructor and take a written exam
More details will be given during the class.
References
Ref 2. J. Leskovec, A. Rajaraman, and J. Ullman, Mining of Massive Datasets, Cambridge University Press.
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