Subject

XSL Content

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

NameInstitutionCategoryDoctorTeaching profileAreaE-mail
ARBELAIZ GALLEGO, OLATZUniversity of the Basque CountryProfesorado AgregadoDoctorBilingualComputer Architecture and Technologyolatz.arbelaitz@ehu.eus
PEREZ RAMIREZ, ALICIAUniversity of the Basque CountryProfesorado AgregadoDoctorBilingualComputer Languages and Systemsalicia.perez@ehu.eus
SOROA ECHAVE, AITORUniversity of the Basque CountryProfesorado AgregadoDoctorBilingualScience of Computation and Artificial Intelligencea.soroa@ehu.eus

Competencies

NameWeight
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

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Lecture-based101525
Applied laboratory-based groups203050

Training activities

NameHoursPercentage of classroom teaching
Lectures25.040 %
Prácticas con ordenador, laboratorio, salidas de campo, visitas externas50.040 %

Assessment systems

NameMinimum weightingMaximum weighting
Attendance and participation10.0 % 10.0 %
Practical tasks30.0 % 60.0 %
Written examination30.0 % 60.0 %

Learning outcomes of the subject

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.

Learn to use specific software for classification in natural language processing tasks.

Temary

1. Introduction to Machine Learning in NLP

2. 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 Press

Data 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