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 data science 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.

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.

Extra-lessons refreshing such notions may be planned in the first part 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 topics is typically related/relevant to the objectives of the Ph.D. studies of the student.

• The project replaces the written part of the examination
• Project description and rules and Q&A.
• Recording of project description (.mp4)

Lessons will be NOT be live-streamed, but recordings of past years are available here for non-attending students.

To watch the recordings online, you must be connected to the unipi.it VPN. Alternatively, right click on the link and download the whole file, then watch it locally on your device using e.g. VLC media player.

Slides and R scripts might be updated after the classes to align with actual content of lessons and to correct typos. Be sure to download the updated versions.