2024
Principles of Artificial Intelligence and Data Science
Name: Principles of Artificial Intelligence and Data Science
Code: INF14386L
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Informatics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
The general objective of this curricular unit is to provide the student with a solid foundation in the fields of Artificial Intelligence (AI) and Data Science (DS). More specifically, and separated by area, the objectives are as follows:
1) Regarding Data Science, the student will know how to implement DS solutions, using basic data analysis and visualization techniques. Furthermore, will know how to plan CD processes, using the most appropriate methodologies in the area. Finally, will have the ability to solve simple and practical problems, using standard DS techniques.
2) Regarding Artificial Intelligence, the student will know how to identify and characterize the main domains of this area of knowledge, with special emphasis on classical AI and Machine Learning. Finally, will know how to apply AI methods, both classic and Machine Learning, to different use cases.
1) Regarding Data Science, the student will know how to implement DS solutions, using basic data analysis and visualization techniques. Furthermore, will know how to plan CD processes, using the most appropriate methodologies in the area. Finally, will have the ability to solve simple and practical problems, using standard DS techniques.
2) Regarding Artificial Intelligence, the student will know how to identify and characterize the main domains of this area of knowledge, with special emphasis on classical AI and Machine Learning. Finally, will know how to apply AI methods, both classic and Machine Learning, to different use cases.
Contents
Introduction to Data Science
- Implementation of Data Science solutions, basic data analysis and visualization.
- Introduction of the Data Science process, and appropriate methodology.
- Examples of core Data Science methods with case studies such as in clustering, classification and regression.
Artificial Intelligence
- Introduction to classical AI and Machine Learning, including the relationship tor areas such as algorithms and optimization, and AI philosophy.
- Examples of methods and applications of AI, in classical AI (search and constraint satisfaction), and ML-based (search engines, Naive Bayes and neural networks)
- Implementation of Data Science solutions, basic data analysis and visualization.
- Introduction of the Data Science process, and appropriate methodology.
- Examples of core Data Science methods with case studies such as in clustering, classification and regression.
Artificial Intelligence
- Introduction to classical AI and Machine Learning, including the relationship tor areas such as algorithms and optimization, and AI philosophy.
- Examples of methods and applications of AI, in classical AI (search and constraint satisfaction), and ML-based (search engines, Naive Bayes and neural networks)
Teaching Methods
Teaching methodologies:
Theoretical classes; laboratory practical classes with problems and exercises that accompany the theoretical material.
Use of LMS (Moodle) as a resource for organizing and accessing learning materials and communicating with teachers and colleagues.
Provision of exercises, of gradual difficulty, covering the topics taught, for the student to practice mastery of the subject.
Collaborative work (in groups) within the development of a project.
Theoretical classes; laboratory practical classes with problems and exercises that accompany the theoretical material.
Use of LMS (Moodle) as a resource for organizing and accessing learning materials and communicating with teachers and colleagues.
Provision of exercises, of gradual difficulty, covering the topics taught, for the student to practice mastery of the subject.
Collaborative work (in groups) within the development of a project.
Assessment
Continuous assessment - consisting of 2 components:
* two frequencies (40% each)
* project (20%)
Final assessment - consisting of 2 components:
* exam (80% each)
* project (20%)
The final grade is obtained through the weighted average of the 2 components. The student is approved if the final grade is equal to or greater than 10.
* two frequencies (40% each)
* project (20%)
Final assessment - consisting of 2 components:
* exam (80% each)
* project (20%)
The final grade is obtained through the weighted average of the 2 components. The student is approved if the final grade is equal to or greater than 10.
Teaching Staff
- João Pedro Lebre Magalhães Pereira [responsible]
Certificações
