First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
COMPUTER SCIENCE
Course unit
PROBABILITY AND STATISTICS
SC03106737, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
COMPUTER SCIENCE
SC1167, Degree course structure A.Y. 2011/12, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination PROBABILITY AND STATISTICS
Website of the academic structure http://informatica.scienze.unipd.it/2018/laurea
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge FRANCESCA COLLET MAT/06
Other lecturers CLAUDIO FONTANA MAT/06

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines MAT/06 Probability and Mathematical Statistics 6.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Practice 2.0 16 34.0 No turn
Lecture 4.0 32 68.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2018/19 Reg.2011 course timetable

Examination board
Board From To Members of the board
3 a.a 2018/2019 01/10/2018 28/02/2020 FORMENTIN MARCO (Presidente)
FONTANA CLAUDIO (Membro Effettivo)
BARBATO DAVID (Supplente)
FISCHER MARKUS (Supplente)
VARGIOLU TIZIANO (Supplente)
2 a.a 2017/2018 01/10/2017 28/02/2019 FORMENTIN MARCO (Presidente)
BARBATO DAVID (Membro Effettivo)
BIANCHI ALESSANDRA (Membro Effettivo)
FERRANTE MARCO (Membro Effettivo)
FISCHER MARKUS (Membro Effettivo)
VARGIOLU TIZIANO (Membro Effettivo)

Syllabus
Prerequisites: Familiarity with the basic notions of analysis, linear algebra and combinatorics. The courses "Analisi matematica" and "Algebra e matematica discreta" cover all the necessary prerequisites.
Target skills and knowledge: The student will acquire a basic knowledge of probability theory and inferential statistics. Those that will pass the exam will be able to build simple probabilistic models of uncertain phenomena and carry out the necessary probabilistic and/or statistical computations.
Examination methods: 3 hour written test, closed book.
Assessment criteria: The student will have to master the theoretical concepts and show his ability to apply them to solve problems of probability and statistics of appropriate difficulty.
Course unit contents: Probability theory. Axioms and their elementary consequences. Examples of discrete, finite, and uniform probability spaces. The birthday problem. Conditional probability. Law of total probability, Bayes formula. Independent events. Random variables and vectors. Joint discrete distributions and densities. Independent random variables. Moments: expectation, variance, higher moments, correlation, covariance. Inequalities: Jensen, Markov, Chebishev. Examples of discrete random variables: Bernoulli, binomial, geometric, Poisson. Poisson limit theorem. Absolutely continuous random variables, uniform, exponential, normal. Weak law of large numbers (Chebyshev). The method of Montecarlo. Central limit theorem (Lindeberg- Lévy). Normal approximation.

Descriptive statistics. Qualitative and quantitative data, relative frequencies, graphical methods. Empirical indices: location, centrality, dispersion, shape. Correlation between variables: regression line, correlation, correlation coefficient.

Inferential statistics. Estimators. Confidence intervals. Statistical tests: hypothesis and alternative, critical region, critical value, type I and type II errors, power, p-value, bilateral and unilateral tests. Tests for the mean and for difference of means. Paired tests. Tests for proportions: contingency tables and chi-square tests.
Planned learning activities and teaching methods: Traditional lectures. A typical lecture consists of theoretical developments, examples and counterexamples, and exercises. Through the moodle platform graded solved exercises are assigned at the end of each lecture.
Additional notes about suggested reading: The lectures cover all the topics on which the exam is based. The moodle platform contains several teaching aids: a set of notes on probability theory, several sheets of graded exercises and their solutions.
Textbooks (and optional supplementary readings)
  • M. Bramanti, Calcolo delle probabilità e statistica. Bologna: Progetto Leonardo, --. Cerca nel catalogo
  • Lorenzo Finesso, Appunti di Probabilità. --: --, --. Available on Moodle