Course Topics
The course offers an overview of the theory of probability in connection to its use in computer science and the use of statistics in analysing and understanding empirical data.

Teaching format
The course combines lectures, exercise groups with instructors, and coursework assignments that are corrected and commented on by Teaching Assistants. In the lectures, the instructor introduces new concepts and techniques using board work and projected material, complemented by short in-class exercises. The assignments give students the opportunity to consolidate these concepts by applying them to selected problems. In the exercise groups, students discuss possible approaches to the assignment tasks with the instructors and compare alternative solutions. They also work on additional problems, independent of the assignments, to deepen their understanding of the material covered in the lectures.

Educational objectives
The course belongs to the type “attività formative di base – matematica-fisica”. The course offers an overview of the theory of probability in connection to its use in computer science and the use of statistics in analysing and understanding empirical data. Knowledge and understanding: • D1.2 - Possess solid knowledge of statistics and probability theory that support computer science and in-depth economic subjects. Applying knowledge and understanding: • D2.1 - Ability to use mathematics and statistical data analysis tools to solve computational problems. Learning skills • D5.1 - Learning ability to undertake further studies with a high degree of autonomy.

Additional educational objectives and learning outcomes
Use of software tools: Students will be able to use R and Java for basic statistical computations. Application of statistical concepts: Students will be able to apply probabilistic and statistical methods to simple real-world scenarios, such as determining sample sizes for surveys, calculating posterior probabilities for diagnostic tests, or assessing the applicability of the central limit theorem in a given situation.

Assessment
The assessment is based on coursework assignments (30%) and a written final exam (70%). Both assignments and the exam consist of groups of questions built around a hypothetical scenario or a mathematical setting. The questions require students to explore different aspects of the scenario or to investigate properties of the setting. Assignments are intended to help students review and deepen the material covered in the lectures, while the exam assesses whether they have achieved the learning outcomes of Knowledge and understanding, Applying knowledge and understanding, and Making judgments.

Evaluation criteria
Assignments and exams are assessed based on the correctness and clarity of the answers. For students who submit all assignments, the final grade is the weighted average of the exam mark (70%) and the assignment mark (30%). If not all assignments are submitted, the assignment component is reduced in proportion to the number of assignments handed in. In addition, if the mark for a question in an assignment is lower than the corresponding exam mark, the higher exam mark will apply.

Required readings

Sheldon M. Ross. Introduction to Probability and Statistics for Engineers and Scientists. Academic Press, London, England. 6th, 2021. ISBN : 0-12-817747-0

Subject Librarian: David Gebhardi, David.Gebhardi@unibz.it

Supplementary readings

Joseph K. Blitzstein, Jessica Hwang. Introduction to Probability. Chapman and Hall/CRC, Boca Raton, USA. 2nd edition, 2019.

ISBN : 978-1-1383-6991-7

Further information
R kann unter folgender Adresse heruntergeladen werden: https://www.r-project.org R Studio, die IDE für die Sprache R, kann unter folgender Adresse heruntergeladen werden: https://posit.co/download/rstudio-desktop/