Course Unit Page
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Teacher Riccardo Rovatti
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Learning modules Riccardo Rovatti (Modulo 1)
Francesco Conti (Modulo 2)
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Credits 6
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SSD ING-INF/01
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Teaching Mode Traditional lectures (Modulo 1)
Traditional lectures (Modulo 2)
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Language English
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Campus of Bologna
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Degree Programme Second cycle degree programme (LM) in Electronic Engineering (cod. 0934)
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Course Timetable from Sep 16, 2022 to Nov 04, 2022
Course Timetable from Nov 08, 2022 to Dec 20, 2022
Academic Year 2022/2023
Learning outcomes
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.
Course contents
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
Teaching methods
Class teaching
Assessment methods
Oral examination
Office hours
See the website of Riccardo Rovatti
See the website of Francesco Conti