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

Knowledge-Based Systems

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Description
We will place emphasis on Knowledge-based Systems from two perspectives that will constitute two blocks of the subject. First, we will focus on search algorithm patterns to understand what kind of problems they can solve, their computational costs, their memory costs, and their limitations. This base will lead us to present symbolic methods of machine learning: Based on analogies (KNN, IBL, CBR), based on decision trees (ID3) and Clustering (Kmeans, Hierarchical grouping and SOM). A second part will focus on the semantic web and linked data. Much of the data used in knowledge-based systems is available on the web thanks to initiatives such as Open Data. To integrate and represent them -and then operate on them- the technologies of the semantic web follow an alternative approach to that of the traditional web by complementing the data with explicit semantics based on formal representations of knowledge: ontologies.
Type Subject
Primer - Obligatoria
Semester
First
Course
1
Credits
5.00

Titular Professors

Elisabet Golobardes Ribé
Professor

Professors

Elisabet Golobardes Ribé
Xavier Vilasís Cardona
Previous Knowledge
Objectives

The goals will focus on:
• Know the scope of artificial intelligence and, specifically, the field of knowledge-based systems.
• Be aware of the computational costs and the quality of the solutions of the different search algorithms.
• Be knowledgeable about basic machine learning methods. Introducing WEKA and Python.
• Be knowledgeable about the semantic web and interlaced data.

Contents

1. Introduction to Artificial Intelligence and Knowledge-Based Systems

2. Problem Solving. Search algorithms
2.1 Problem Solving. Concepts
2.2 Blind search
2.3 Heuristic Search. Heuristics

3. Machine learning (I)
3.1 Paradigms. Concepts.
3.2 Inductive learning. Decision trees
3.3 Analogical learning. KNN. IBL. CBR
3.4 Clustering

4. Semantic web and ontologies.
4.1 Introduction to the concept of knowledge engineering
4.2 Technologies of the Semantic Web
4.3 Ontology development
4.4 Storage and queries of semantic data with SPARQL
4.5 Linked Open Data

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

Methodology

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 conceptual exercises, written exercises, and oral 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:
- AI (search algorithms and artificial learning): 70%
- Semantic web and ontologies: 30%

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:
- AI (search algorithms and artificial learning): 70%
- Semantic web and ontologies: 30%

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. Some references:

• Class/Lecture notes
• M. Ginsberg. "Essentials of Artificial Intelligence". Morgan Kaufmann Publishers (1993)
• E. Golobardes and A. Orriols. "Intel·ligència artificial. Guia d'estudi". Creative Commons Deed (2008)
• N.J. Nilsson. "Artificial Intelligence: A New Syntesis". Morgan Kaufmann Publishers, Inc. (Last Version)
• E. Rich and K. Knight. "Inteligencia Artificial". McGrawHill (Last versión)
• S. Russell and P. Norvig. "Artificial Intelligence. A Modern Approach". Prentice Hall International Editions (Last version)

Additional Material

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