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 |
|---|---|
| Ability to understand and apply the basic statistical measures for the description of characteristics in a data set. | 35.0 % |
| Ability to understand machine learning strategies in human language processing. | 25.0 % |
| Ability to apply classic algorithms for solving NLP problems. | 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 |
Training activities
| Name | Hours | Percentage of classroom teaching |
|---|---|---|
| Computer work practice, laboratory, site visits, field trips, external visits | 50.0 | 40 % |
| Lectures | 25.0 | 40 % |
Assessment systems
| Name | Minimum weighting | Maximum weighting |
|---|---|---|
| Attendance and participation | 10.0 % | 10.0 % |
| Practical tasks | 30.0 % | 60.0 % |
| Written examination | 30.0 % | 60.0 % |
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.
* To know how the algorithms work in order to apply the most appropriate one to each problem.
* To know the proper preprocessing of the input data to raise and adequately solve the classification problem.
* To use specific software for natural language processing tasks classification
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