OVERVIEW

Computational and Statistical Methods Laboratory (LMCS, code 90741) is worth 6 credits and takes place in the first semester of the 3rd year of the three-year degree (L-30)
 

AIMS AND CONTENT

LEARNING OUTCOMES

Teaching aims to consolidate and expand skills in calculus, statistical analysis and programming, aimed at data analysis and acquisition in laboratory experiences.

AIMS AND LEARNING OUTCOMES

The course deals with computational physics, addressing the numerical solution of ordinary differential equations and partial derivatives, and advanced methods of data analysis, dealing with Monte Carlo simulation rudiments, deepening the best-fit techniques and giving an overview of multivariate signal/background separation techniques.

The course also aims to extend the C++ programming skill (acquired during the first year) by more in-depth study of Object-Oriented programming in C ++ and providing rudiments of Python and shell scripting. Higher level packages will be also used (ROOT, Octave / Matlab).

At the end of the course the student will be able, exploiting C ++, python or Matlab programming, to:

• solve numerically ordinary and partial differential equations
• build simple simulations or integrations with the Monte Carlo method
• perform chi-square and maximum likelihood fits of various levels of complexity
• evaluate confidence intervals and perform hypothesis tests

PREREQUISITES

The course assume that computation skills contained in the first year coures are acquired.

TEACHING METHODS

Lectures and laboratory exercises.

SYLLABUS/CONTENT

Lectures schedule:

•  OO programming (inheritance, polymorphism), shell scripting and Python, use of specific packages / libraries (ROOT, Octave / Matlab)
•  Numerical solution of ordinary differential equations. Applications to classical physics and quantum mechanics problems (Numerov method for the Schrodinger equation).
•  Numerical solution of partial differential equations. Applications to eletromagnetism and heat propagation.
•  Introduction to the generation of random variables and Monte Carlo simulation
•  Extraction of quantities of interest from a data sample: binned and unbinned likelihood. Point estimate, confidence intervals. Hypothesis testing. Limits.
•  Overview of multivariate classification techniques (Likelihood ratio, neural networks).

RECOMMENDED READING/BIBLIOGRAPHY

Notes / slides are provided during the course. A list of possible texts for further information is available on the course page on Aulaweb.

TEACHERS AND EXAM BOARD

Exam Board

ROBERTA CARDINALE (President)

STEFANO PASSAGGIO

FABRIZIO PARODI (President Substitute)

CARLO SCHIAVI (Substitute)

FEDERICO SFORZA (Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The final exam consists of two parts:

• computer exam
• oral exam

The exercises carried out and the tasks assigned during the year will contribute to the final evaluation.

ASSESSMENT METHODS

ASSESSMENT METHODS

The final grade is the average of the computer-based examination score, increased by the continuous assessment score, and the oral examination score, according to the following formula:

Final grade = 0.5 × (computer-based examination score + continuous assessment score) + 0.5 × oral examination score.

CONTINUOUS ASSESSMENT

There are 4 homework assignments, each graded from 0 to 3. The average of these scores constitutes the continuous assessment score and is added to the computer-based examination score.

Attendance at the 7 practical sessions and 4 hands-on sessions is compulsory. To be admitted to the computer-based examination, students must have attended at least 80% of the practical and hands-on sessions. Students may make up a maximum of two missed sessions; the first part of the sixth practical session cannot be made up.

COMPUTER-BASED EXAMINATION

The computer-based examination consists of developing programs to solve 2 problems chosen from the 3 provided, within a maximum of 150 minutes.

To be admitted to the oral examination, students must achieve a combined score of at least 16, calculated as the sum of the computer-based examination score and the continuous assessment score.

ORAL EXAMINATION

The oral examination consists of two questions covering different aspects of the course syllabus. During the examination, the examiners assess the candidate’s understanding of the fundamental course content and ability to explain it clearly.

FURTHER INFORMATION

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sergio Di Domizio (sergio.didomizio@unige.it), the Department’s disability liaison.