Titular Professors
The previous knowledge will be those of the subject of Data Analysis and Visualization Tools (first semester).
Objectives.
Complete the knowledge of statistical methods for data analysis, as for example, Monte Carlo simulations, function fitting, learner evaluation, time series
In other to consolidate the acquired knowledge, a project will be developed, in common with the subject MD008 - Artificial Intelligence, with the aim of solving a specific problem applying all the techniques based on statistics or machine learning seen so far.
1. Exploratory data analysis
2. Concept of model
3. Learner evaluation
4. Montecarlo methods
5. Theory of decisions
6. Aproximation of distributions
7. Regressions
8. Time series
9. Fitting functions
10. Hypothesis testing and confidence intervals
Note: Topics can be adjusted and/or modified at the discretion of the master's coordination.
The methodology used combines master classes, student participation, exercises, and practices. For the student, this will involve both individual and group works, as well as exercises of implementation, written report and oral presentations.
This subject will be assessed on a continuous via from exercises, assignments, practices, and presentations in class.
An important part of the assessment consists of a project developed in common with the subject of Artificial Intelligence (MD008).
Continuous assessment
This subject will be assessed on a continuous via from exercises, assignments, practices, and presentations in class. An important part of the assessment consists of a project developed in common with the subject of Artificial Intelligence (MD008).
The final grade will be a weighting of two blocks:
FINAL GRADE = 40% Exercises + 60% Project.
Extraordinary call
The exam and/or works of extraordinary call will be determined from the coordination of the subject.
Copies regulations
The subject is governed by the general regulations of copies of La Salle Campus BCN:
https://www.salleurl.edu/en/copies-regulation
The training activities will be considered to have the following category:
Exercises: moderately significant
Project: highly significant
Final Evaluation: highly significant
The bibliography will be detailed throughout the course.
Class/Lecture notes
Documentation and papers uploaded to Intranet (eStudy)
All class material (presentations, exercises, articles, documents, etc.) will be shared in the subject folder of the La Salle Intranet: eStudy.