2023
Topics on Artificial Intelligence and Data Science
Name: Topics on Artificial Intelligence and Data Science
Code: INF14727M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Informatics, Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
Provide a comprehensive and up-to-date overview of fundamental topics in Artificial Intelligence and Data Science from the perspectives of statistics and computing. Emphasis will be given to the fundamentals as well as the techniques and methods of Artificial Intelligence and Data Science.
At the end of the course, students should be able to:
* know the fundamental general concepts of the areas of Artificial Intelligence and Data Science
* know basic approaches, techniques and methods of data analysis, solving specific problems and performance evaluation
* critically analyze the performance of possible solution proposals
* know how to design and develop solutions to solve a specific problem
At the end of the course, students should be able to:
* know the fundamental general concepts of the areas of Artificial Intelligence and Data Science
* know basic approaches, techniques and methods of data analysis, solving specific problems and performance evaluation
* critically analyze the performance of possible solution proposals
* know how to design and develop solutions to solve a specific problem
Contents
Introduction to Artificial Intelligence (AI)
Paradigms: Induction, deduction and abduction
types of problems and approaches
Examples of AI methods and applications
Introduction to Data Science
Machine learning systems design
- Problem framing
- Data acquisition
- Data discovery and visualization.
- Data pre-processing for machine learning
- Model selection and training
- Model tuning
- Launch monitoring and maintenance of machine learning systems
Case studies
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
- Active learning
- Unstructured data
- Data Streams
- Big data
Paradigms: Induction, deduction and abduction
types of problems and approaches
Examples of AI methods and applications
Introduction to Data Science
Machine learning systems design
- Problem framing
- Data acquisition
- Data discovery and visualization.
- Data pre-processing for machine learning
- Model selection and training
- Model tuning
- Launch monitoring and maintenance of machine learning systems
Case studies
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
- Active learning
- Unstructured data
- Data Streams
- Big data
Teaching Methods
During the lectures, the course contents defined will be exposed, as detailed as possible, reinforcing the dialogue with students about the importance of using the acquired knowledge.
Contact and interactivity with students, as well as the availability of support materials will be used on the learning platform of the University of Évora (moodle). A data science challenge and competition management platform will be used to solve problems and challenges (kaggle: https://www.kaggle.com/).
Contact and interactivity with students, as well as the availability of support materials will be used on the learning platform of the University of Évora (moodle). A data science challenge and competition management platform will be used to solve problems and challenges (kaggle: https://www.kaggle.com/).
Assessment
Continuous assessment:
theoretical (50%): obtained through 2 frequencies
practice (50%): group work/project
Final assessment:
theoretical (50%): obtained through an exam
practice (50%): group work/project
theoretical (50%): obtained through 2 frequencies
practice (50%): group work/project
Final assessment:
theoretical (50%): obtained through an exam
practice (50%): group work/project
Teaching Staff
Certificações
