A background on probability is assumed. Subject Statistics for Research MR012 is strongly recommended to follow the sessions.
Students are expected to acquire and develop the following abilities:
1. Basic general knowledge on the subject.
2. Capacity to analyse specific problems and synthesise acquired knowledge.
3. Specific problem solving.
4. Capacity to apply knowledge on practical situation.
5. Capacity to learn and individually broaden knowledge.
6. Decision making in complex environments in the ICT framework from a systematic analysis.
1. What is Data Analysis? What can we expect from data? - Result validation
2. Sampling
3. Clustering
4. Bayesian Inference
5. Probability Density Function Fitting
6. Information Measures and Entropy
7. Principal Component Analysis
8. Independent component analysis
9. Data Complexity metrics
10. Function approximation
11. Introduction to dynamical systems
12. Time series analysis
The subject is structured into one weekly session of 1.5h of theory thought in the traditional classroom lesson method plus an additional 1.5h session in which examples, cases and topic extensions will be developed and studied. These sessions will also be used to introduce students to statistical software.
Evaluation shall rely on class attendance and participation and a set of written assignments to be timely delivered. Students with low class attendance or not delivering the assignments shall undergo a final exam.
The final mark will depend on the quality of the delivered papers, a minimal amount of class attendance and participation.
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No text is proposed.