Artificial Intelligence

The teaching methods (ME) to be used are balanced between traditional and active and are as follows:
ME1 Content exposure by the teacher (compatible with learning objectives 1 to 7)
ME2 Test the contents learned by students (compatible with learning objectives 1 to 7)
ME3 Student Problem Solving (Compatible with Learning Objectives 4 to 8)
ME4 Interaction and sharing of ideas by students (compatible with learning objective 8)
ME5 Development of critical thinking by students (compatible with learning objectives 7 and 8)
ME6 Research done by students (compatible with learning objectives 4 to 8)
ME7 Student-made creation (compatible with learning objectives 8)
The curricular unit is based on theoretical-practical classes. The teaching methods (ME) to be used are balanced between traditional and
active.
Classes include the presentation of concepts and methodologies and proceed with their discussion, as well as the demonstration of the
resolution of applied problems. In the classes concepts and methodologies are presented, contents are discussed and problem solving is
demonstrated. The content is taught and discussed in a classroom environment.
In addition to the traditional expository method, the methodology will include project-based learning (PBL). As the name implies, an active
learning methodology that aims to associate learning with doing. This method is based on the construction of knowledge collectively,
moving away from the conventional classroom model where the teacher teaches a subject and the students show how much they have
learned from a final evaluative activity. The project that is proposed to be developed, preferably carried out in a group, aims to go through
the various phases of an artificial intelligence project.

The main learning objectives (AO) defined are the following:
LO1 – Know the principles of artificial intelligence
LO2 – Know the principles of knowledge representation and inference
LO3 – Understand the working principles of expert systems
LO4 – Know the main tasks and activities of artificial intelligence
LO5 – Know the main techniques and algorithms of artificial intelligence
LO6 – Knowing some tools and technologies and knowing how to use some of them
LO7 – Know how to evaluate the quality of solutions and know how to validate these solutions
LO8 – Know how to apply in a practical project some of the main concepts and approaches learned
The teaching methods (ME) to be used are balanced between traditional and active and are listed in Topic 8.

1 Introduction to artificial intelligence
1.1 History of artificial intelligence
1.2 Principles of artificial intelligence, machine learning and deep learning
1.3 Weak, strong and superintelligence artificial intelligence
1.4 Discovery of knowledge in databases and data mining
2 Knowledge and inference
3 expert systems
4 Main tasks and activities
4.1 Predictive (or supervised) activities
4.2 Descriptive (or unsupervised) activities
4.3 Prescriptive Activities
5 Main techniques and algorithms
5.1 Induction of decision trees
5.2 Artificial neural networks
5.3 Genetic algorithms
5.5 Rule Induction
5.5 Fuzzy sets
5.6 Bayes Networks
5.7 Other techniques and algorithms
6 Tools and Technologies
7 Quality and validation of solutions

NAO

Adamson, J. (2021). Minding the Machines: Building and Leading Data Science and Analytics Teams. John Wiley & Sons.
Berry, Michael J, & Linoff, Gordon. (1997). Data Mining Techniques: For Marketing, Sales, and Customer Support. New York, USA: John
Wiley & Sons, Inc.
Bigus, Joseph P. (1996). Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support.
Crawfordsville, Indiana, USA: McGraw-Hill, Inc.
Chakrabarti, Soumen, Cox, Earl, Frank, Eibe, Güting, Ralf Hartmut, Han, Jiawei, Jiang, Xia, Neapolitan, Richard E. (2008). Data Mining:
Know It All. Burlinghton, Massachusetts: Morgan Kaufmann Publishers.
Fernandes, Anita Maria da Rocha. (2005). Inteligência Artificial: Noções Gerais. Brasil: Visual Books.
Hotz, N. (2022, October 7). What is a Data Science Life Cycle?. Data Science Process Alliance. https://www.datascience-pm.com/datascience-
life-cycle/
Kelleher, J. D., Mac Namee, B., & D’arcy, A. (2015). Fundamentals of machine lea