PROBABILITY AND STATISTICS

Academic year 2018/2019 Syllabus of previous years
Official course title PROBABILITA' E STATISTICA
Course code CT0111 (AF:248799 AR:136462)
Modality Frontal Lesson
ECTS credits 6
Degree level Bachelor's Degree Programme
Educational sector code SECS-S/01
Period 1st Semester
Course year 2
Where VENEZIA
Contribution of the course to the overall degree programme goals
The course is one of the quantitative training activities of the Bachelor's Degree Programme in Informatics. The aim is to get the student familiar with the main probabilistic methods for use in computer science.
The course provides knowledge of probability, as well as skills in the use of specific programs for probabilistic calculus, simulation and reporting.
At the end of the course, the student will be able to identify suitable models and methodologies in the context of interest; moreover he will learn to interpret and communicate the obtained results on simple reports.
Expected learning outcomes
1. Knowledge and understanding:
- to know the basic concepts of elementary probability, probability distributions and limit theorems
- to know the main tools for calculus and graphical representation of one-dimensional and multivariate probability distributions
- to know and understand the practical importance of Monte Carlo simulation methods

2. Ability to apply knowledge and understanding:
- to use specific programs for working with probability distributions and for simulation
- to use appropriate formulas and terminology in all the processes of application and communication of the acquired knowledge

3. Ability to judge:
- to apply the acquired knowledge in a specific context, identifying the most appropriate probabilistic models and methods

4. Communication skills:
- to present in a clear and exhaustive way the results obtained from solving a probability problem, using rigorous formulas and appropriate terminology

5. Learning skills:
- to use and merge information from notes, books, slides and practical lab sessions
- to assess the achieved knowledge through quizzes, exercises and assignments during the course
Pre-requirements
Knowledge of mathematics at the level of Calculus 1 and 2
Contents
Probability: sample space, events and probability; conditional probability and independence; discrete and continuous random variables; expectation and moments; joint distributions of random variables, covariance and correlation; convergence of random variables and limit theorems; Markov chains; simulation and Monte Carlo methods.
The use of the software R (http://cran.r-project.org/ ) is part of the programme of the course and the main tool for solving the assignments and for the exam.
Referral texts
Suggested books:
M. Boella (2011). Probabilità e statistica per ingegneria e scienze. Pearson Italia, Milano.
G. Espa, R. Micciolo (2014). Problemi ed esperimenti di statistica con R. Apogeo.
H. Hsu (2011). Probabilità, variabili casuali e processi stocastici. McGraw-Hill.
R.A. Johnson (2007). Probabilita' e statistica per ingegneria e scienze. Prentice Hall.
W. Navidi (2006). Probabilità e statistica per l'ingegneria e le scienze. McGraw-Hill.
S.M. Ross (2016). Calcolo delle probabilità. Terza edizione. Apogeo.
S.M. Ross (2015). Probabilità e statistica per l'ingegneria e le scienze. Terza edizione. Apogeo.
Assessment methods
The achievement of the course objectives is assessed through participation in activities and assignments during the course (30%) and a short final exam in the lab (70%). For those students that do not take part on the assignments during the course, the whole evaluation is based on the full final exam (100%).
The use of the software R is part of the program of the course and the main tool for solving the assignments and for the exam. Examples of quizzes and exercises will be available in Moodle.
Activities and assignments consist on the solution of quizzes and exercises proposed in Moodle. The solution to some exercises is in the form of short reports.
The final exam takes place in the computer lab and is composed of quizzes and exercises to be solved with R. The exercises are similar to those assigned in Moodle during the course and are thought to assess the expected learning results.
Teaching methods
Theoretical lectures and exercises at the blackboard and in the lab, using R. Use of Moodle platform for learning assessment.
Teaching language
Italian
Type of exam
written
Definitive programme.
Last update of the programme
17/09/2018