Subject
Machine Learning (II)
General details of the subject
- Mode
- Face-to-face degree course
- Language
- English
Description and contextualization of the subject
El curso pone el foco en un conjunto de t¿icas inspiradas en la inteligencia artificial y la estad¿ica. En la ¿ltima d¿da, estos campos han experimentado un crecimiento notable, particularmente relacionado con el an¿sis de grandes cantidades de datos mediante t¿icas y algoritmos de base matem¿ca, estad¿ica y de optimizaci¿eur¿ica. La aplicaci¿e t¿icas de aprendizaje autom¿co est¿mpliamente expandido en ¿as como la bioinform¿ca, finanzas, y tambi¿el procesamiento de textos.El alumnado estudiar¿as principales t¿icas para la miner¿de datos, y aumentar¿us habilidades en usos de populares herramientas de software que implementan estas t¿icas. Todo ello mediante la demostraci¿obre aplicaciones reales de procesamiento de texto.
Teaching staff
Name | Institution | Category | Doctor | Teaching profile | Area | |
---|---|---|---|---|---|---|
INZA CANO, IÑAKI | University of the Basque Country | Profesorado Pleno | Doctor | Bilingual | Science of Computation and Artificial Intelligence | inaki.inza@ehu.eus |
Competencies
Name | Weight |
---|---|
Habilidad para manejar las estrategias y herramientas basadas en conocimiento para el procesamiento del lenguaje humano. | 30.0 % |
Habilidad para el manejo y la adaptación de los métodos simbólicos y basados en corpus (aprendizaje automático) más relevantes para la investigación en las tecnologías de la lengua. | 70.0 % |
Study types
Type | Face-to-face hours | Non face-to-face hours | Total hours |
---|---|---|---|
Lecture-based | 10 | 15 | 25 |
Applied computer-based groups | 20 | 30 | 50 |
Learning outcomes of the subject
Conocimiento de los principales escenarios de aprendizaje autom¿co.Identificar el tipo de t¿ica a aplicar en cada escenario de clasificaci¿Conocer los pasos b¿cos, standard, de un pipeline-flujo de an¿sis de datos,
Uso de librer¿ de R-project para la creaci¿e un corpus y su "document-term matrix" asociada, y la posterior aplicaci¿e t¿icas de aprendizaje autom¿co sobre ella.
Temary
1- General terms on the "data science" world: the "data science" term, relation among AI and data science, the big data term, kaggle repository, kdnuggets.com, data science for a better world...2- Principal classification scenarios: supervised classification, unsupervised classification (clustering), weakly supervised classification (alternative scenarios). For each learning scenario: structure of the data matrix, type of annotation, real world applications.
3- Semi-supervised classification: usefulness in NLP tasks. Software, RSSL package in R.
4- One-class classification and outlier detection: usefulness in NLP tasks. Software, R packages.
5- Using statistical tests to compare the accuracy of different classifiers. Software: R, online statistical tests in the web
6- Feature selection techniques. Techniques for selecting a "competitive" subset of original features.
7- General techniques and filters for data preprocessing. Preprocessing filters for any kind of data: missing data imputation, one-hot encoding, discretization, imbalanced class distributions...
8- "A short introduction to the tm (text mining) package in R: text processing". How to construct by text mining operators a proper corpus, and transform to a document-term matrix for further machine learning analysis. Starting from raw text such as files, html pages, twitter... A tutorial using R software.
9- "The machine learning approach: clustering words and classifying documents with R". A tutorial using R software, caret package.
10 - "First steps on deep learning for NLP by R’s h2o package (+word2vec)". A tutorial using R software. Voluntary work
Bibliography
Basic bibliography
*M. Kuhn, K. Johnson (2013). Applied Predictive Modeling. Springer.*ParallelDots, online text analysis APIs for several tasks: sentiment analysis, tags' prediction, keyword generator, entity extraction, comparing similarity of texts, different emotions analysis, intent analysis, abusive text prediction, etc. https://www.paralleldots.com/text-analysis-apis
* sentiment140: an interesting project for automatic sentiment categorization of tweets: http://help.sentiment140.com/
* Stanford TreeBank project. "Recursive deep models for semantic compositionality over a semantic treebank". https://nlp.stanford.edu/sentiment/
* RDataMining website: Text mining with R: Twitter data analysis: http://www.rdatamining.com/docs/text-mining-with-r
* Awesome sentiment analysis: A curated list of Sentiment Analysis methods, implementations and misc. https://github.com/xiamx/awesome-sentiment-analysis
* "5 things you need to know about sentiment analysis and classification": https://www.kdnuggets.com/2018/03/5-things-sentiment-analysis-classification.html
* Bing Liu's website on "Opinion mining, sentiment analysis and opinion spam detection: the machine learning approach". https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
* 18 NLP key terms, explained for ML practitioners and NLP novices: https://www.kdnuggets.com/2017/02/natural-language-processing-key-terms-explained.html