Predmet: ELBA_Dushanbe

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- Forum
  
  Announcements Forum
- Forum
  
  Discussion Forum
- Datoteka
  
  Re-training program (tentative) Datoteka
- Datoteka
  
  Re-training agenda (tentative) Datoteka
- Teachers of this re-training (from UP):
  
  Assist. prof. Branko Kavšek, Ph.D. Assistant Professor in Computer Science at The Faculty of Mathematics, Natural Sciences and Information Technologies (UP FAMNIT) – University of Primorska in Koper and Researcher at the Artificial Intelligence Laboratory (AI LAB) – “Jožef Stefan” Institute in Ljubljana.
  His research includes machine learning, data mining, association rules, graphs, time series and data analysis in general. He closely collaborates with researchers from the Artificial Intelligence Laboratory and the Department of Information Sciences and Technologies.
  He teaches courses Formal Languages and Computability, Introduction to Machine Learning and Data Mining, and Intelligent Systems to both undergraduate and master’s students at the university where he is the coordinator of the undergraduate study program Computer Science and vice head of the Department of Information Sciences and Technologies.
  
  Assoc. prof. Jernej Vičič, Ph.D. Associate Professor in Computer Science at The Faculty of Mathematics, Natural Sciences and Information Technologies (UP FAMNIT) – University of Primorska, and Senior Researcher at the Research Centre of the Slovenian Academy of Sciences and Arts. He received Ph.D. in Computer Science at the University of Ljubljana, Faculty of Computer Science and Informatics with the thesis "A Fast Implementation of Rules-Based Machine Translation Systems for Similar Natural Languages" in August 2012.
  He is the head of the Blockchain and Language Technologies Lab at UP FAMNIT.
  His major research interests involve language technologies in general, specifically, he focuses on the field of Natural Language Translation and Distributed Ledger Technologies.
  Among his major achievements in the presented fields (apart from his Ph.D. thesis) is the machine translation system for the related languages: Slovenian – Croatian based on the open-source Apertium translation system toolkit. Jernej is one of the developers of the Apertium toolkit.
Before starting - software requirements & instalation
- Install the WEKA Data Mining Toolbox on your machine. You may find the installation instructions here:
  https://waikato.github.io/weka-wiki/downloading_weka/#developer-version
  (we will be using the Developer Version of WEKA - version 3.9.6).
The data, ETL, visualization
- Data repositories++:
  
  UCI ML: https://archive.ics.uci.edu/ml/index.php
  Kaggle: https://www.kaggle.com/
  OpenML: https://www.openml.org/
  FigShare: https://figshare.com/
- Slides and short quizzes:
- Datoteka
  
  The CRISP-DM standard Datoteka
- Kviz
  
  What have I learned? Kviz
- Datoteka
  
  Input concepts Datoteka
- Kviz
  
  What have I learned? Kviz
- Datoteka
  
  Visualization Datoteka
- Kviz
  
  What have I learned? Kviz
- Datoteka
  
  Introduction to statistics Datoteka
- Kviz
  
  What have I learned? Kviz
- Datoteka
  
  Data preparation Datoteka
- Kviz
  
  What have I learned? Kviz
- Tutorials and hands-on exercises:
- Mapa
  
  Tutorial #1: The Iris data set -- selected formats Mapa
- Naloga
  
  Exercise #1: A selected UCI ML data set in ARFF format Naloga
- Mapa
  
  Tutorial #2: EDA and data preparation Mapa
- Naloga
  
  Exercise #2: EDA and preparation on UCI ML Labor data set Naloga
Corpora
- Datoteka
  
  Corpora Datoteka
- Datoteka
  
  POS tagging / corpus tagging Datoteka
- Datoteka
  
  Terminology Datoteka
- Datoteka
  
  Text mining Datoteka
- Stran
  
  Sketch engine workshop Stran
Supervised learning - Classification
- Slides and short quizzes:
- Datoteka
  
  Simple classification methods Datoteka
- Kviz
  
  What have I learned? Kviz
- Datoteka
  
  Decision trees -- introduction Datoteka
- Kviz
  
  What have I learned? Kviz
- Datoteka
  
  Decision trees -- the C4.5 algorithm Datoteka
- Kviz
  
  What have I learned? Kviz
- Datoteka
  
  Decision rules -- the covering algorithm Datoteka
- Kviz
  
  What have I learned? Kviz
- Tutorials and hands-on exercises:
- Mapa
  
  Tutorial #3: Classification basics and Decision trees Mapa
- Naloga
  
  Exercise #3: Classification algorithms -- basics Naloga
- Mapa
  
  Tutorial #4: Classification -- the C4.5 algorithm Mapa
- Naloga
  
  Exercise #4: Using J4.8 to model sample data Naloga
- Mapa
  
  Tutorial #5: Decision rules -- the covering algorithm Mapa
- Naloga
  
  Exercise #5: Using the PRISM algorithm to learn decision rules Naloga
Evaluation
- Slides:
- Datoteka
  
  Evaluation Datoteka
- Tutorials and hands-on exercises:
- Mapa
  
  Tutorial #6: Evaluation Mapa
- Naloga
  
  Exercise #6: Comparing different evaluation strategies and measures Naloga
Miscellaneous - Regression & KNN

Highlighted
- Slides:
- Datoteka
  
  Regression and nearest neighbors Datoteka
- Tutorials and hands-on exercises:
- Mapa
  
  Tutorial #7: Regression and nearest neighbors Mapa
- Naloga
  
  Exercise #7: Linear regression, the kNN algorithm Naloga

Nedelja	Ponedeljek	Torek	Sreda	Četrtek	petek	Sobota
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Ni dogodkov, Sunday, 26. February Ni dogodkov, Sunday, 26. February	Ni dogodkov, Monday, 27. February Ni dogodkov, Monday, 27. February	Ni dogodkov, Tuesday, 28. February Ni dogodkov, Tuesday, 28. February

Oris teme

Splošno

Before starting - software requirements & instalation

The data, ETL, visualization

Corpora

Supervised learning - Classification

Evaluation

Miscellaneous - Regression & KNN

February 2023