- Docente: Riccardo Rovatti
- Crediti formativi: 6
- SSD: ING-INF/01
- Lingua di insegnamento: Inglese
- Moduli: Riccardo Rovatti (Modulo 1) Luca Benini (Modulo 2)
- Modalità didattica: Convenzionale - Lezioni in presenza (Modulo 1) Convenzionale - Lezioni in presenza (Modulo 2)
- Campus: Bologna
- Corso: Laurea Magistrale in Ingegneria elettronica (cod. 0934)
Conoscenze e abilità da conseguire
The course provides students with a basic knowledge of problems and corresponding techniques of solutions implied by the ever increasing amount and complexity of the data available for analyses and decisions, i.e., the so called Big-Data (BD). The corresponding issues are tackled by multiple points of view: from the abstract characterization of the mathematical properties of BD, to the hardware architectures needed to process them.
Contenuti
The two dimensions of "Big" in Big Data.
Data dimensionality
- geometrical effect of high dimensionality and consequences
Dimensionality reduction
- multidimensional Gaussian vectors and their properties
- dimensionality reduction by Johnson-Lindenstrauss
- dimensionality reduction by SVD/PCA (relationship with Gaussian clustering)
- dimensionality reduction by sparse signal recovery/compressed sensing
- other uses of SVD/eigenstructures: the hub-authority ranking, the pagerank core idea, document collection summaries)
Interpolation
- grid-data multilinear interpolation
- grid-data piecewise-linear interpolation
- scattered-data interpolation by radial-basis functions
Streaming algorithms
- the streaming computation model
- streaming random picks and multiplication of huge matrices
- streaming estimation of features of occurences histogram
- hashing for flattening of distributions
- random computation: estimations instead of exact results
Metodi didattici
Class teaching
Modalità di verifica e valutazione dell'apprendimento
Oral examination
Orario di ricevimento
Consulta il sito web di Riccardo Rovatti
Consulta il sito web di Luca Benini