Numerical Methods for Computer Science (12 cfu -Part II) - inactive course
Syllabus
Syllabus of italian course "Metodi Numerici per l'Informatica" (until aa 2016/17)
Important: Students NEED to enroll the exam via ESSE3. Any problem occurring with ESSE3 has to be reported immediately. Only students listed on ESSE3 system would take the exam.
Exam Timetable
Utilities
Notes and slides
PCA
Notes for first and second lectures on PCA
A Tutorial on Principal Component Analysis, Jonathon Shlens (Google Research) ver.2014
SVD-based principal component analysis of geochemical data, Petr Praus, Central European Journal of Chemistry, 3 (4), pp. 731-741, Springer, 2005
Eigenfaces for recognition ( M. Turk, A. Pentland, Journal of Cogn. Neuroscience 3-1 1991)
VSM
Vector Space Model for Information Retrieval (Berry, Drmac, Jessup, SIAM review 1999)
Web-IR (new)
Slides for lectures on Hits and Pagerank
Review on eigenmethods for Web Retrieval (Langville, Meyer, SIAM review 2005)
Multivariate Polynomial Regression in Data Mining: Methodology, Problems and Solutions (P. Sinha, JISER 2013)
NMF for clustering: a survey (Li, Ding, 2013)
Dataset for Labs
Real data for polynomial least square approximation (explanation draft and data)
List of project topic (optional)
Application of SVD to image compression
Application of PCA to explorative analysis of multivariate datasets
Application of PCA to image compression and evaluation of PSNR and Entropy of reconstructed data
Eigenface model
Construction of a DEMO for interpreting eigenfaces obtained from a given set of training color images
Least squared approximation (by linear regression line) of 2D data
Polynomial approximation of 2D data with Cross-Validation for choosing best polynomial degree
Multiple line regression: application on selected datasets and discussion of the obtained results
Principal Component Regression
Exploratory data analysis of multivariate benchmark datasets
Links
V. Comincioli, Metodi Numerici e Statistici per le Scienze Applicate
M. W. Berry , M. Browne, Understanding Search Engines: Mathematical Modeling and Text Retrieval, Second Edition
Metodi di Ottimizzazione (italian notes)
Supplementary materials
The $25,000,000,000 eigenvector. The linear algebra behind Google. SIAM Review, 48 (3), 569-81. 2006
Italian Slides (for the italian course until aa 2016/2017)
Information Retrieval (Understanding Search Engine di M.B. Berry e M. Brown)
Lucidi utilizzati a lezione per discutere il VSM (NEW aggiornato il 27/04/17)
Lucidi utilizzati a lezione per discutere i metodi per il WIR (NEW aggiornato il 2/05/17)
Data Mining: alcune tecniche matematiche (new aggiornato il 2 maggio 2016)
NMF an overview (Blocco 1 e Blocco 2) (new aggiornato il 2 maggio 2016)
Appunti sui minimi quadrati ( a cura del professor L. Lopez, 2008)
Understanding search engine: metodi e metodi matematici (approfondimenti tratti dal testo Understanding Search Engines: Mathematical modeling and Text )