Master of Science in Data Science La Salle Campus Barcelona URL

Master of Science in Data Science

Become an expert in analysing, structuring, filtering, visualizing and valuing the production of generated data

Artificial Intelligence for Data Science

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Description
In Data Science it is essential to apply Artificial intelligence, specifically Machine learning, as data analysis methods. These methods allow the knowledge of the knowledgebase to be made explicit. In this subject, as a continuation of the subject “MD005 Knowledge-Based Systems”, deep machine learning methods will be worked on (e.g., Bagging / Boosting, Evolutionary Computation and Deep Learning), and other methods that were not introduced in Semester 1 (such as Support Vector Machines or Association Rules). Likewise, a project will be carried out together with the subject “MD009 - Advanced data analysis tools” applying all the techniques based on statistics or machine learning seen so far, to solve a specific problem. It will be like a mini-Master thesis done in a group.
Type Subject
Primer - Obligatoria
Semester
Second
Course
1
Credits
5.00

Titular Professors

Elisabet Golobardes Ribé
Professor

Professors

Elisabet Golobardes Ribé
Xavier Vilasís Cardona
Xavier Vilasís Cardona
Míriam Calvo Gómez
Angel Berian González
Andrey Ustyuzhanin
Previous Knowledge

The subjects of the Data Science module of the first semester or equivalent. Basic programming knowledge.

Objectives

The goals will focus on:
• Know the scope of Artificial Intelligence, specifically Machine Learning, in Data Science.
• Be aware of the computational requirements and the quality of the solutions of the different methods.
• Be knowledgeable about deep machine learning methods. Bagging & Boosting, Deep Learning and Evolutionary Computation. Using Python and its libraries.
• Be knowledgeable about a global map: what technique to use, given a problem and given a set of data.

Contents

SYLLABUS

1. Bagging & Boosting
2. Neural Networks. Deep learning
3. Evolutionary computing. Genetic algorithms
4. Support Vector Machines
5. Association Rules
6. Project (joint project with the subject MD009 - Advanced data analysis tools)

Note: Topics can be adjusted and/or modified at the discretion of the master's coordination.

Methodology

The methodology used combines master classes, the resolution of exercises, student participation and the development of a project. For the student, this will involve both individual and groups works, as well as conceptual exercises, implemented exercises, oral presentations, and written presentations.

Evaluation

Continuous assessment
This subject will be assessed on a continuous via from exercises, assignments, practices, and presentations in class. The final grade will be a weighting of two blocks:
- Exercises: 60%
- Project: 40%

Evaluation Criteria

Continuous assessment
This subject will be assessed on a continuous via from exercises, assignments, practices, and presentations in class. The final grade will be a weighting of two blocks:
- Exercises: 60%
- Project: 40%

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

Basic Bibliography

The bibliography will be detailed throughout the course.

• Class/Lecture notes
• Documentation and papers uploaded to Intranet (eStudy)

Additional Material

All class material (presentations, exercises, articles, documents, etc.) will be shared in the subject folder of the La Salle Intranet: eStudy.