Embedded Files
Academic Year 2026-27
Timetable:
TBA
Office Hours: Tuesday 16-18.
Instructor
Brunero Liseo brunero.liseo@uniroma1.it
Course Organization
The course will begin during the week of 22 February. Attendance is not compulsory; however, it is strongly recommended.
The course consists of two lectures per week. In addition, a laboratory session will be scheduled after every three lectures. Coding activities and practical exercises will be carried out using the R software environment.(available from the instructor).
Assessment and Grading
The final grade is determined by the combination of two separate assessment components:
Individual take-home assignment (30% of the final grade): students are required to complete an individual assignment based on a dataset provided by the instructor.
Written examination (70% of the final grade).
Students who do not submit the take-home assignment must replace that component with an oral examination covering the entire course syllabus.
Textbook:
Slides and notes available from the instructor
Wasserman, L., & Verdinelli, I. (2026). All of Regression. Cambridge University Press.
Another useful book
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2021) An Introduction to Statistical Learning with R (2nd edition) available at https://www.statlearning.com/
Warning: Colours in the figures are important! Then, print a color copy of the book or read it on a tablet!
There is a companion R package available at
https://cran.r-project.org/web/packages/ISLR/index.html
Other suggested readings
Azzalini, A. and Scarpa, B. (2012) Data analysis and data mining, Oxford University Press
Detailed Program:
Linear Regression
Nonparametric Regression
Logistic and Poisson Regression
High-Dimensional Regression
Quantile Regression
Conformal Prediction
Classification
Scheduled dates of exam
June 14th 2027
July 12th 2027
September 9 2027
January 17 2028
February 7 2028
Page updated
Report abuse