Degree | Type | Year |
---|---|---|
4313136 Modelling for Science and Engineering | OT | 0 |
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An elementary knowledge in Probability Theory and Statistical Inference.
Course of R . All the practical exercises will be solved using the statistical package R. This introductory course is basic for all the posterior developements.
Visualization of large-scale datasets with R. GViz, Maps and Tabplot.
Data Simulation, Boostrapping and Permutation testing. These methodologies allow a fast solution to complex statistical models without a deep knowledge of the general and classical statistical topics. They are indispensable tools in the current statistical modelling techniques. The students will complete a basic training program, including the use of an appropriate software, and they will learn how to attack several real data analysis problems.
Bayesian networks. In the opinion of many researchers one of the most significant contribution in AI in this century, are graphical structures for representing the probabilistic relationships among a large number of variables and for doing probabilistic inference with those variables, with a huge number of application fields. One of the objectives of this course is to introduce them and develop in students some skill in their use in modelling, both from a theoretical and applied point of view, with particular emphasis on the use of appropriate software.
Part 1: Introduction to R (6h)
Part 2: Visualization of large-scale datasets with R (6h)
Part 3: Bayesian Networks (13h)
1) Block 1: Basics.
2) Block 2: Causal networks and Inference in Bayesian networks.
3) Block 3: Learning Bayesian network parameters.
Part 4: Data Simulation, Boostrapping and Permutation testing (13h)
1) Permutation tests.
2) Jackknife.
3) Parametric Bootstrap.
4) Non-parametric Bootstrap.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Exercises | 16 | 0.64 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 |
Lectures | 38 | 1.52 | 4, 5, 6, 7, 8, 10 |
Projects + Assigments | 18 | 0.72 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 |
Type: Supervised | |||
Practical sessions | 20 | 0.8 | 4, 5, 6, 7, 8, 10 |
In this course lectures, in which the determining factor is the teacher's explanation, are the basis of the learning process. It is also very important the participation of the students, combined with practical sessions in which it is the student him/herself who must use the knowledge to solve problems.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Daily homework | 50 | 38 | 1.52 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 |
Projects | 50 | 20 | 0.8 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 |
The evaluation of the course consists in a continuous assessment.
There are 4 assessments during the course, weighted as 10%, 10%, 40%, 40% corresponding to each part.
Each professor will explain his or her own type of assessment.
Part 1 assessment: Daily homework + final project (individual simple real data analysis with R).
Part 2 assessment: Daily homework + final project.
Part 3 assessment: Daily homework + delivery of some exercises + final project.
Part 4 assessment: Daily homework + delivery of some exercises.
The R programming language will be utilized.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(TEm) Theory (master) | 1 | English | first semester | afternoon |