mds:smd:2019

**Salvatore Ruggieri**- Università di Pisa

**Office hours**- Tuesday h 14:00 - 17:00, Department of Computer Science, room 321/DO.

Day of Week | Hour | Room |
---|---|---|

Monday | 14:00 - 16:00 | Fib-N1 |

Tuesday | 9:00 - 11:00 | Fib-A1 |

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 are *mandatory text books*:

**[T]**F.M. Dekking C. Kraaikamp, H.P. Lopuha, L.E. Meester.**A Modern Introduction to Probability and Statistics**. Springer, 2005.**[R]**P. Dalgaard.**Introductory Statistics with R**. 2nd edition, Springer, 2008.

- The project can be done in groups of at most 3 students.
- The project must be completed by end of July, including oral discussion (on project and all topics of the course).
- The project replaces the written exam but
**students have to register for the written dates in order to fill the student's questionnaire**. - Google Drive project directory (accessible only to authorized students)

* There are no mid-terms.* The exam consists of a written part and an oral part. The written part consists of exercises on the topics of the course. Each question is assigned a grade, summing up to 30 points. Students are admitted to the oral part if they receive a grade of at least 18 points. Written exam consists of open questions and exercises. Example written texts:

Date | Hour | Room |
---|---|---|

13/1/2020 | 11:00 - 13:00 | Fib-C1 |

5/2/2020 | 11:00 - 13:00 | Fib-C1 |

Date | Room | Topic | Learning material | |
---|---|---|---|---|

1 | 18.02 14:00-16:00 | N1 | Introduction. Probability and independence. | [T] Chpts. 1-3 |

2 | 19.02 9:00-11:00 | A1 | R basics. | [R] Chpts. 1,2.1,2.2 slides script1.R |

3 | 26.02 9:00-11:00 | A1 | Discrete random variables. | [T] Chpt. 4 [R] Chpt. 3 script2.R |

4 | 4.03 14:00-16:00 | N1 | Continuous random variables. Simulation. | [T] Chpts. 5, 6.1-6.2 [R] Chpt. 3 script3.R |

5 | 5.03 9:00-11:00 | A1 | Expectation and variance. R data access. | [T] Chpt. 7 [R] Chpt. 2.4 script4.R |

6 | 12.03 9:00-11:00 | A1 | Recalls: derivatives and integrals. | [P] Chpt. 1-8 scriptMath.R |

7 | 13.03 11:00-13:00 | I-Lab | Power laws and Zipf laws. | Newman's paper Sects. I,II,IIIA,IIIB,IIIE,IIIF script5.R |

8 | 18.03 14:00-16:00 | N1 | Zipf laws. Project presentation. | |

9 | 19.03 9:00-11:00 | A1 | R programming. | [R] Chpt. 2.3 exercise.R script6.R |

10 | 20.03 11:00-13:00 | I-Lab | Computations with random variables. Joint distributions. | [T] Chpts. 8-9 script7.R |

11 | 25.03 14:00-16:00 | N1 | Covariance. Sum of random variables. | [T] Chpts. 10-11 |

12 | 26.03 9:00-11:00 | A1 | Law of large numbers. The central limit theorem. | [T] Chpts. 13-14 script8.R |

13 | 8.04 14:00-16:00 | N1 | Graphical summaries. | [T] Chpt. 15 script9.R |

14 | 9.04 9:00-11:00 | A1 | Numerical summaries. Data preprocessing in R. Q&A on the project. | [T] Chpt. 16, [R] Chpts. 4,10 script10.R, dataprep.R |

15 | 15.04 14:00-16:00 | N1 | Unbiased estimators. Efficiency and MSE | [T] Chpts. 17.1-17.3, 19, 20 script11.R |

16 | 16.04 9:00-11:00 | A1 | Maximum likelihood. Fisher information. | [T] Chpt. 21 notes1.pdf |

17 | 29.04 14:00-16:00 | N1 | Linear, polynomial, and non-linear regressions and least squares. | [T] Chpts. 17.4,22 [R] Chpts. 6,12.1,16.1-16.2 script12.R |

18 | 30.04 9:00-11:00 | A1 | Confidence Intervals: Gaussian, T-student, large sample method. | [T] Chpts. 23.1,23.2,23.4,24.3,24.4 script13.R |

19 | 7.05 9:00-11:00 | A1 | Empirical bootstrap. Application to confidence intervals. | [T] Chpts. 18.1,18.2,23.3 script14.R |

20 | 8.05 11:00-13:00 | I-Lab | Parametric bootstrap. Hypotheses testing. | [T] Chpts. 18.3,25 script15.R |

21 | 20.05 14:00-16:00 | N1 | One-sample t-test and application to linear regression. | [T] Chpts. 26-27, [R] Chpts. 5.1,5.2 script16.R |

22 | 22.05 11:00-13:00 | Sem.Ovest | Goodness of fit: chi-square, K-S. Fitting power laws. audio-video recording of the lesson | K-S script17.R |

23 | 28.05 9:00-11:00 | A1 | Hypotheses testing: F-test, comparing two samples. | [T] Chpts. 28, [R] Chpts. 5.3-5.7 script18.R |

24 | 29.05 11:00-13:00 | I-Lab | Project tutoring. |

mds/smd/2019.txt · Ultima modifica: 31/01/2020 alle 10:21 (8 settimane fa) da Salvatore Ruggieri