IP (A) (Tools for Large Database Analysis)

Weekly outline

• 

  General

  Lectures @FRI are on Tuesdays 17:00-19:15 in P03.

  
  

  USEFUL LINKS / KORISTNE POVEZAVE
  

  Course website / Spletna stran predmeta: http://web.stanford.edu/class/cs246/

  Important info / Pomembne informacije:
  

  • handouts / povzetek (PDF): http://web.stanford.edu/class/cs246/handouts/CS246_Info_Handout.pdf

  
  

  Classes / Predavanja

  • 2023: https://snap.stanford.edu/class/cs246-videos-2023/
    username: snap, password: cs246-spring2023-videoarchive

  • 2022: https://snap.stanford.edu/class/cs246-videos-2022/
    username: cs246, password: mining2022

  • 2019: http://snap.stanford.edu/class/cs246-videos-2019/
    
  • 2018: http://snap.stanford.edu/class/cs246-videos-2018/

  Additional materials / Dodatna gradiva: https://web.stanford.edu/class/cs246/index.html#schedule

  Reference text / Knjiga: http://www.mmds.org/

  
  

  Weekly Colab notebooks: 
  

  • you will find them directly on the http://web.stanford.edu/class/cs246/ website,
  • they are posted every Thursday,
  • due one week later on Thursday 23:59 Pacific Time (PT), but rather submit earlier!
  • submit via this website (below).

  
  

  Assignments and grading:

  • 4 homework assignments requiring coding and theory (40%)
  • Final exam (30%)
    
  • Weekly Colab notebooks (30%)
    

  
  

  More about the course is on the CS246 Stanford web page. All deadlines on FRI are exactly the same as Stanford deadlines.

  
  

  • Course Announcements 2023 Forum
  • CS246 Final Exam, Winter 2016 File 249.4KB Dokument PDF
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  Prosojnice in dodatna gradiva / Slides and supplementary materials

  • Spark tutorial

    This Jupyter notebook is based on the lecture "CS246: Mining Massive Datasets: Crash Course in Spark" by Daniel Templeton.
    

  • Jupyter notebook: Spark Tutorial File 39.8KB Datoteka z besedilom
  • Jupyter notebook: Spark Tutorial (HTML) File 713.1KB Dokument HTML
  • Frequent itemsets mining and Association rules

  • Pogosti nabori predmetov in povezovalna pravila File 1.1MB Dokument PDF
  • Jupyter notebook: Finding frequent itemsets with the Apriori algorithm File 14.4KB Datoteka z besedilom
  • Locality sensitive hashing

  • Finding Similar Items: Locality Sensitive Hashing (CS246 2022) File 2.3MB Dokument PDF
  • Theory of Locality Sensitive Hashing (CS246 2022) File 2.1MB Dokument PDF
  • Visualization of the effect of threshold, band size, and # of rows in LSH by Trenton Chang.
    

  • Clustering

  • Odkrivanje skupin File 2.6MB Dokument PDF
  • Odkrivanje skupin z omejitvami File 1.3MB Dokument PDF

    Potrebe po izboljšanju odkrivanja skupin vedno bolj zahtevajo možnost interakcije z domenskimi eksperti, kar je vodilo do razvoja algoritmov odkrivanja skupin z omejitvami (angl. constrained clustering). Ti algoritmi uporabljajo domensko znanje v obliki pozitivnih (angl. must-link) in negativnih omejitev (angl. cannot-link) na pare učnih primerov, kar omogoča izboljšanje procesa odkrivanja skupin...
    

  • Dimensionality reduction

  • SVD demo File 15.7KB Preglednica Excel 2007
  • Eigenvalues and Eigenvectors of a Matrix by Ben Kuttler
    

  • Recommender systems

  • Priporočilni sistemi File 1.6MB Dokument PDF
  • Link Analysis

  • PageRank & HITS File 1.1MB Dokument PDF
  • PageRank & HITS - simulacija File 27.5KB Preglednica Excel 2007
  • Graphs

  • Iskanje maksimalne klike v proteinskem grafu File 3.6MB Dokument PDF

    V magistrskem delu, ki je rezultiralo v objavi spodnjega članka v ugledni znanstveni reviji Mathematics, uporabimo moderne pristope strojnega učenja na grafih za pohitritev dinamičnega algoritma za iskanje maksimalne klike.

    Kristjan Reba, Matej Guid, Kati Rozman, Dušanka Janežič, and Janez Konc. 
    Exact maximum clique algorithm for different graph types using machine learning. 
    Mathematics 10, no. 1 (2022): 97.

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  Colab notebooks

  Submit Colab notebooks here; every week no later than Friday 9 am.
  

  Your submission should contain a ZIP file:

  • Jupyter notebook in HTML format (download the jupyter notebook file and then use the command "jupyter nbconvert <file_name.ipynb>" in the command prompt).
    
  • A text/PDF file with answers to the questions (the submission page will always contain a document with questions). 

  
  

  Each file should use the following naming convention: colab<number>_<name>_<surname>.

  • Colab 0 Assignment
  • Colab 1 Assignment
  • Colab 2 Assignment
  • Colab 3 Assignment
  • Colab 4 Assignment
  • Colab 5 Assignment
  • Colab 6 Assignment
  • Colab 7 Assignment
  • Colab 8 Assignment
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  Homework Submissions

  Your submission (every second Friday 9:00 CET) should be a ZIP file containing three files:

  • file <name>_<surname>.pdf: written report.
  • file <name>_<surname>.zip: all the requested code. Use subfolders ("q1", "q2", ...) for partiqular questions. Include at least .ipynb and .html files, .py files are welcome too.
  • Cover sheet (make sure you state your collaborators and the date of submission).

  Note that you submit the homeworks only here on Ucilnica. You do not submit anything to GradeScope or SNAP website!
  
  

  Late days: you are allowed to use the “late days” twice with your homework (but only once per particular homework!). Do not submit your homework later than Tuesday 9:00 CET, the first Tuesday after the regular deadline.
  

  • Cover sheet File
  • HW1 submission (code & report) Assignment
  • HW2 submission (code & report) Assignment
  • HW3 submission (code & report) Assignment

    Rok za oddajo je petek, 19. maja ob 9h.
    

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  Exam

  
  

  • CS246 Final Exam, Winter 2019 File 1.4MB Dokument PDF
  • CS246 Final Exam, Winter 2018 File Uploaded 7/03/22, 22:45
  • CS246 Final Exam, Winter 2016 File Uploaded 7/03/22, 22:46
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  Dodatno

  Not available