This is the home page of a Ph.D. level course offered at the National Ph.D. in AI - Society. The program covers the basic methodologies, techniques and tools of statistical analysis. This includes basic knowledge of probability theory, random variables, convergence theorems, statistical models, estimation theory, hypothesis testing, bayesian inference, causal reasoning. Other topics covered include bootstrap, expectation-maximization, and applications to machine learning problems.

The course is an extract of the M.Sc. level course Statistics for Data Science.

• Salvatore Ruggieri
  • Università di Pisa
  • http://pages.di.unipi.it/ruggieri/
  • salvatore [dot] ruggieri [at] unipi [dot] it
  • Office hours: Tuesdays h 14:00 - 16:00 or by appointment, at the Department of Computer Science, room 321/DO, or via Teams.

The course will be offered in one week to be fixed in the period February 2025 - May 2025. A Teams channel will be used to post news, notes, Q&A, and other stuff related to the course. The lectures will be live-streamed and recorded.

IMPORTANT: the actual week of the course will be published here in January 2025. Please send an email to salvatore [dot] ruggieri [at] unipi [dot] it if you have any preference about the week in the period February 2025 - May 2025

Students should be comfortable with most of the topics on mathematical calculus covered in:

• [P] J. Ward, J. Abdey. Mathematics and Statistics. University of London, 2013. Chapters 1-8 of Part 1.

Recording to extra-lessons refreshing such notions will be available prior to the starting of the course.

The following is the mandatory text book:

• [T] F.M. Dekking C. Kraaikamp, H.P. Lopuha, L.E. Meester. A Modern Introduction to Probability and Statistics. Springer, 2005.

Some running examples will be provided using the R programming language. However, knowledge of R is not required nor mandatory for the exam.

• R
• R Studio

Ph.D. students may do an exam in the form of a report on an advanced topic/survey to be agreed upon. The topic is typically related/relevant to the objectives of the Ph.D. studies of the student.