Numerical Methods for Computer Science (12 cfu -Part II) - inactive course

FAQ (for Italian course Metodi Numerici per l'Informatica" (until aa 2016/17)

Lecture Timetable

Syllabus

• Provisional Syllabus AA 2018-2019

• Detailed topics AA 2018-19 (25 February-16 May)

• Syllabus of  italian course "Metodi Numerici per l'Informatica" (until aa 2016/17)

• Detailed topics AA. 2017-18

• Programma AA 2016-2017

• Programma AA 2015-2016

• Programma AA 2014-2015

• Programma AA 2013-2014

• Programma AA 2012-2013

• 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

  • EDA and basic visual representation of multivariate data   

  • PCA 

    • Notes for first and second lectures on PCA

      • A Tutorial on Principal Component Analysis,  Jonathon Shlens (Google Research) ver.2014 

  •         (Annotated version)

    • • 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 

    • Slides for lectures on 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

  • Immages for SVD compression 

  • Dataset Iris

  • Dataset Indiani Pima

  • Dataset Wine

  • Dataset Community and Crime

    • Image Dataset useful for Eigenface model

    • Bike Sharing (linear regression) 

    • Wine Quality (linear regression)

    • 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 

• C. Meyer, Matrix Analysis and Applied Linear Algebra

• 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

  • Ian Rogers, Page-rank explained

  • 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

• The Mathematics of the web search

• The Page-Rank Citation Ranking: Bringing order to the web

• 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 )