Course: 2022/2023

Data Processing

(14311)

Requirements (Subjects that are assumed to be known)

This is a first term course, so no other courses of the Master programme are key for this course. However, it is highly desirable that students are familiarized with basic concepts from statistics.

After this course students will understand the principles underlying the general regression, classification and data analysis problems, and will become familiarized with the different approaches for dealing with them. Students will learn that, for the correct understanding of these problems, it is necessary to master three basic probability theory elements: 1) the likelihood, 2) the difference between a priori and a posteriori uncertainty, and 3) Bayes' Theorem.
From a practical point of view, students will be presented different approaches for learning from data to solve these problems: non-parametric techniques, methods based on empirical risk minimization, or those that follow Bayesian principles.
More specifically, the following list summarizes the main objectives of this course, enumerated as competences to be acquired by the students:
- knowledge of the theoretic principles underlying several of the most important techniques for learning from data.
- ability to apply such techniques on real problems, and to extract results and conclusions.
- understanding of classic methods for estimation and classifications, and skills for their correct application.
- ability to use machine learning tools: gaussian processes, support vector machines, non-parametric methods
- knowledge of other data analysis problems, like topic modeling or recommendations systems

Skills and learning outcomes

Description of contents: programme

Unit 0: Introduction to data processing
Unit 1: Data preprocessing
1.1. Data normalization
1.2. Dimensionality reduction
1.3. Clustering
Unit 2: Regression
2.1. The regression problem
2.2. Non-parametric regression: k-NN
2.3. Linear and polynomial least squares regression
2.4. Bayesian regression
2.5. Other regression algorithms
Unit 3: Classification
3.1. Classification problema
3.2. Non-parametric methods: k-NN
3.3. Logistic regression
3.4. Neural Networks.
3.5. Other classification algorithms
Unit 4: Topic models
4.1. Text analysis
4.2. The Latent Dirichlet Allocation (LDA) algorithm

Learning activities and methodology

LECTURES AND PRACTICAL SESSIONS
Theory sessions consist of lectures in which the basic concepts of the course will be introduced, illustrating them with a large number of examples. Exercises and problems similar to those to be proposed in the exam will also be solved along the course.
LAB SESSIONS
Sessions in which students will apply the concepts presented in the course with the help of a computer. Students will deal with real data analysis problems, and will have to evaluate the performance of the implemented systems

Assessment System

- % end-of-term-examination 50
- % of continuous assessment (assigments, laboratory, practicals...) 50

Basic Bibliography

- R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification (2nd ed.). Wiley Interscience. 2001

- Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola · Dive into Deep Learning : https://d2l.ai/
- J. Cid-Sueiro, J. Arenas-García, V. Gómez-Verdejo · Machine Learning 4 All : https://github.com/ML4DS/ML4all

Additional Bibliography

- A.C. Müller, S. Guido. Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc. 2016
- C. M. Bishop. Pattern Recognition and Machine Learning. Springer. 2006
- Trevor Hastie, Robert Tibshirani, Jerome H. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Series in Statistics. 2009

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The course syllabus may change due academic events or other reasons.