Subject
Statistics and Mathematics for NLP
General details of the subject
- Mode
- Face-to-face degree course
- Language
- English
Description and contextualization of the subject
Introduction to basic concepts of statistics in the field of natural language processing. Inferential aspects will be worked on, including the most common statistical tests, as well as interval estimation. Furthermore, basic concepts of functions and linear algebra will be introduced for Natural Language Processing tasks with the final aim of gaining understanding of the rationale behind neural networks.Teaching staff
| Name | Institution | Category | Doctor | Teaching profile | Area | |
|---|---|---|---|---|---|---|
| EZEIZA RAMOS, NEREA | University of the Basque Country | Profesorado Agregado | Doctor | Bilingual | Computer Languages and Systems | n.ezeiza@ehu.eus |
| IRIGOYEN GARBIZU, ITZIAR | University of the Basque Country | Profesorado Agregado | Doctor | Bilingual | Science of Computation and Artificial Intelligence | itziar.irigoien@ehu.eus |
Competencies
| Name | Weight |
|---|---|
| Ability to understand and apply the appropriate statistical tests according to the objectives set, given a set of data. | 35.0 % |
| Ability to understand basic mathematical language. | 25.0 % |
| Ability to understand the underlying intuition in vector spaces and perform basic calculations. | 40.0 % |
Study types
| Type | Face-to-face hours | Non face-to-face hours | Total hours |
|---|---|---|---|
| Lecture-based | 10 | 15 | 25 |
| Applied laboratory-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
Pose the correct hypotheses according to the objectives and characteristics of the data to perform statistical tests.Interpret the results of a statistical test and complement it with estimation by intervals.
Know the definition of what a function is. Know the concept of derivative. Perform basic calculations of linear algebra.
Learn to use specific software to perform calculations related to natural language processing tasks.
Ordinary call: orientations and renunciation
Final exam to assess the subject.Extraordinary call: orientations and renunciation
Final exam to assess the subject.Temary
1. Introduction to hypothesis testing: independence test, Mc Nemar test2. Real function. Concept of derivative
3. Matrix calculus
Bibliography
Basic bibliography
R.H. Baayen (2008). Analyzing Linguistic Data. A Practical Introduction to Statistics using R. Cambridge University PressG. Strang (2019). Linear Algebra and Learning from Data. Cambridge University Press
A. Trask (2019). Grokking deep learning. Manning Publications Co.