Checking date: 15/07/2023


Course: 2023/2024

Advanced Modelling
(19147)
Master in Computational Social Science (Plan: 472 - Estudio: 375)
EPC


Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Data Programming (19138) Statistics and Data Science I (19140) Statistics and Data Science II (19141)
Objectives
- Ability to use relevant machine learning concepts and methods to formulate, structure and solve practical problems involving massive or complex data. - Ability to apply basic machine learning models for prediction and decision making.
Skills and learning outcomes
Description of contents: programme
1. Introduction to Machine Learning 1.1. To explain or to predict? 1.2. Bias vs Variance 1.3. Performance evaluation 2. Unsupervised Learning 2.1. Dimensionality reduction: PCA 2.2. Clustering: k-means, hierarchical methods 3. Supervised Learning 3.1. Classification: statistical learning (Bayesian classifiers), machine learning (nearest neighbors, decision trees, random forest, gradient boosting, neural networks) 3.2. Advanced Regression: model selection, regularization tools, feature selection 4. Case Studies for all the topics
Learning activities and methodology
Training Activities: - Theoretical-practical classes Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Spriger. 2021
  • K. Murphy. Probabilistic Machine Learning: An Introduction. MIT Press. 2022

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


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