Fundamentals of Programming and Knowledge Extraction


COURSE DESCRIPTION

Teaching: 12 hours
ECTS: 3

Course Aim: Clearly understand basic concepts related to programming, algorithms, usage of computers and knowledge extraction (data mining). Adapt basic programming methods for text processing, technical programming and knowledge extraction.

Course Content: Data types and structures, Turing machine, algorithms and their complexity, compilers, interpreters, object programming. Structure of computer memory and its effect on program development.
Programming for text processing (HTML, LaTeX, XML).
Basics of technical computing (Matlab).
Clustering of data (graphs) by search (k-means method), Fiedler vector and principal component analysis, vector space model and applications to textual data.
Image compression using singular value decomposition.
Introduction to compressive sensing.

Generic and Specific Competences: Ability to analyze algorithmic problems with respect to their complexity and to choose an adequate solution method. Understanding text processing programs and ability to use them. Understanding technical programs and ability to design and apply programs for data analysis. Understanding algorithms for knowledge extraction and ability to use them in research.


FIRST HOMEWORK - HTML and JavaScript

SECOND HOMEWORK - Sorting in Matlab, LaTeX


LITERATURE

1. Mitra S, Acharya T, Data Mining - Multimedia, Soft Computing and Bioinformatics, John Wiley & Sons, 2003.
2. Sigmon K, MATLAB Primer, Third Edition, University of Florida, Gainesville, 1993. (pdf)
3. Oetiker T, Partl H, Hyna I, Schlegl E, The Not So Short Introduction to LaTeX2e, 1999. (pdf)
4. Berry MW, Drmač Z, Jessup ER, Matrices, Vector Spaces and Information Retrieval, SIAM Review, 41 (1999) 335-362. (pdf)
5. The Java Tutorials (link)
6. Ding HQ, Zha H, He X, Husbands P, Simon HD, Link analysis: Hubs and Authorities on the World Wide Web, SIAM Review, 46 (2004) 256-269. (pdf)
7. Langville AN, Meyer CD, A Survey of Eigenvector Methods for Web Information Retrieval, SIAM Review, 47 (2005) 135-161. (pdf)
8. Compressive sensing tutorials and reviews by Emmanuel Candès, Richard Baraniuk, Emmanuel Candès and Michael Wakin, Justin Romberg, and Dana Mackenzie, from the Rice University Compressive Sensing Resources page.
9. A Beginner's Guide to HTML (pdf)

1. Computing, computation, computers and computer science.
   L1-Computers_and_Computing.pdf
2. Knowledge, problem solving, computing tools, algorithms, Turing machine, complexity.
   L2-Algorithms.pdf
3. Algorithm, program, machine instuctions, interpreters and compilers, data, structure of computer memory and consequences.
   L3-Programming.pdf
4. Scripting languages - server side (PHP) and client side (JavaScript).
   PHP from Wikipedia   PHP from W3Schools
   JavaScript from Wikipedia   JavaScript from W3Schools
5. Programming for text processing (HTML, CSS, LaTeX, XML).
   HTML from Wikipedia   HTML from W3Schools
   A Beginner's Guide to HTML
   CSS from Wikipedia   CSS from W3Schools
   LaTeX from Wikipedia
   The Not So Short Introduction to LaTeX2e
   XML from Wikipedia   XML from W3Schools
6. Basics of technical computing (Matlab, Octave).
   MATLAB Primer, Third Edition   Octave Users' Guide
   Octave On-line ( experimental!)
   Matrix multiplication   Linear independence   Matrix rank
   Vector norm   Matrix norm
   LU factorization   Eigenvalue decomposition
   Singular value decomposition
7. Least squares method - linear regression.
   Linear least squares   Introduction - textbook
8. Clustering of data (k-means algorithm, Fiedler vector and principal component analysis).
   K-means from Wikipedia   K-means (in Croatian)
   Fiedler vector   An example (in Croatian)
   Programs
9. Vector space model and application to textual data.
10. Image compression via singular value decomposition.
    Example program
11. Introduction to compressive sensing.
    Student presentation
12. SEMINAR WORK.