nireEHU Subject
Introduction to Automatic Learning
General details of the subject
- Mode
- Face-to-face degree course
- Language
- English
Description and contextualization of the subject
This is an elementary course for students without any background on data mining. First we will address elementary aspects in the areas of descriptive statistics. We will also introduce machine learning techniques, including basic data processing and the main learning algorithms. The course provides a basic application-case on computational linguistics (e.g. sentiment analysis, spam detection, etc.) to learn elementary vectorial representations for textual information and understand their limitations.Teaching staff
| Name | Institution | Category | Doctor | Teaching profile | Area | |
|---|---|---|---|---|---|---|
| ARBELAIZ GALLEGO, OLATZ | University of the Basque Country | Profesorado Agregado | Doctor | Bilingual | Computer Architecture and Technology | olatz.arbelaitz@ehu.eus |
| PEREZ RAMIREZ, ALICIA | University of the Basque Country | Profesorado Agregado | Doctor | Bilingual | Computer Languages and Systems | alicia.perez@ehu.eus |
Competencies
| Name | Weight |
|---|---|
| Capacidad de comprender y aplicar las medidas estadísticas básicas para la descripción de características en un conjunto de datos. | 35.0 % |
| Capacidad para comprender estrategias de aprendizaje automático en el procesamiento del lenguaje humano. | 25.0 % |
| Capacidad de aplicar algoritmos clásicos para la resolución de problemas de PLN. | 40.0 % |
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
* To know the appropriate preprocessing of the input data in order to set and be able to adequately solve the classification problem.* Learn to use specific software for classification in natural language processing tasks.
* Extract the most important features of statistical variables, such as measures of central tendency, dispersion and correlation, both for quantitative and qualitative variables.
Ordinary call: orientations and renunciation
Sistema de Evaluación ContinuaHerramientas y porcentajes de calificación:
* Prueba escrita a desarrollar (%): 30
* Trabajos prácticos (%): 60
* Asistencia y participación (%): 10
Sistema de Evaluación Final
Herramientas y porcentajes de calificación:
* Prueba escrita a desarrollar (%): 100
Extraordinary call: orientations and renunciation
Sistema de Evaluación FinalHerramientas y porcentajes de calificación:
* Prueba escrita a desarrollar (%): 100
Temary
1. Introduction to Machine Learning in NLP2. Basic descriptive statistics
3. Basic Machine Learning algorithms
4. Evaluation in supervised learning
Bibliography
Basic bibliography
R.H. Baayen (2008) Analyzing Linguistic Data. A Practical Introduction to Statistics using R. Cambridge University PressData Mining. Mark Hall, Ian Witten and Eibe Frank (4th Edition). TheMorgan Kaufmann, 2017.
Machine Learning for Text. Charu C. Aggarwal. Springer, 2018
Fundamentals of Predictive Text Mining (2nd Edition). Weiss, SholomM., Indurkhya, Nitin, Zhang, Tong. Springer-VerlagLondon, 2015
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