| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Science | ||
| DATA SCIENCE | ||
Course unit
STOCHASTIC METHODS
SCP7079197, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
STOCHASTIC METHODS
SCP7079197, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
Information on the course unit
| Degree course |
Second cycle degree in DATA SCIENCE SC2377, Degree course structure A.Y. 2017/18, A.Y. 2018/19 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | 6.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | STOCHASTIC METHODS | |
| Website of the academic structure | http://datascience.scienze.unipd.it/2018/laurea_magistrale | |
| Department of reference | Department of Mathematics | |
| Mandatory attendance | No | |
| Language of instruction | English | |
| 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 | PAOLO DAI PRA | 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 | First semester |
|---|---|
| Year | 1st Year |
| Teaching method | frontal |
| Type of hours | Credits | Teaching hours |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| Lecture | 6.0 | 48 | 102.0 | No turn |
Examination board
Examination board not defined
| Prerequisites: | Basic notions of differential and integral calculus, linear algebra and probability. |
| Target skills and knowledge: | The aim of this course in to introduce tools from the theory of Probability and Stochastic Processes that have high impact in the study of networks as well as algorithmic and computational tools. Using the software R (R development Core Team, 2006), specific problems will be dealt with via computer simulation. |
| Examination methods: | Written exam |
| Assessment criteria: | The final grade is based on the results in the written exam. In the exam student are asked to implement in specific applications the tools learned in the course. Correctness and efficiency will be particularly valued. |
| Course unit contents: | 1. Probability reviews.
• discrete and continuous distributions • random variables, expectation and conditional expectation • approximation of probability distributions. 2. Markov chains and random walks • Markov Chain and their stationary distribution • Monte Carlo (MCMC), convergence of MCMC-based algorithms • Electrical networks. 3. Random graphs • Erdos-Renyi graphs: connectivity, giant component. • Random regular graphs • Dynamic graphs. Preferential attachment. |
| Planned learning activities and teaching methods: | Frontal lessons. Some problems will be solved in classroom via computer simulation |
| Additional notes about suggested reading: | The teacher in charge will provide lecture notes, exercises and scientific papers |
| Textbooks (and optional supplementary readings) |
|
- Moodle (files, quizzes, workshops, ...)
- One Note (digital ink)