Introduction to Artificial Intelligence

Code:

M.IA013

Acronym:

IIA

Keywords
Classification	Keyword
OFICIAL	Informatics Engineering
OFICIAL	Computer Science

Instance: 2025/2026 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Artificial Intelligence

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.IA	12	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

This course provides a set of topics that are the core of the Artificial Intelligence and Intelligent Systems area.

Learning outcomes and competences

This course provides a set of subjects (topics) that are the core of the Artificial Intelligence and Intelligent System area. The main objectives are:
1. Understand the fundamentals of Artificial Intelligence and Intelligent Systems, what characterizes and distinguishes them and their applicability.
2. Being able to design and implement Agents and Multi-Agent Systems to solve different problems.
3. To learn heuristic and systematic methods of problem solving, with and without adversaries and optimization algorithms.
4. To learn methods of acquisition, representation and reasoning with uncertain knowledge using different formalisms.
5. To know and be able to use machine-learning algorithms applying different paradigms (supervised, evolutionary, reinforcement, deep learning).
6. To understand advanced topics in Artificial Intelligence and be able to formulate a vision into the future of AI.
7. To develop simple but complete projects using AI techniques

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Program

1. Introduction to Artificial Intelligence (AI)
Definition of AI; Fundamentals, Scope, Evolution and Chronology of AI; AI Applications.
2. Intelligent Agents
Concept of Agent; Environments; Agent Architectures; Multi-Agent Systems.
3. Problem Solving Methods
Problem Formulation. State Space. Search Strategies and A*; Search with Adversaries and Minimax.
4. Optimization and Metaheuristics
Formulation of Decision/Optimization Problems; Metaheuristics; Genetic Algorithms; Constraint Satisfaction.
5. Knowledge Engineering
Knowledge Acquisition, Representation and Reasoning.
6. Machine Learning
Unsupervised Learning; Supervised Learning; Neural Networks and Support Vector Machines; Reinforcement Learning; Deep Learning.
7. Advanced Topics in Artificial Intelligence
The Future of AI. IA and the Society. Beneficial IA. Explainable AI, Machine Ethics. Weak and Strong IA. Super Intelligence. The Singularity.

Mandatory literature

Russell, S. & Norvig, P. ; Artificial Intelligence: A Modern Approach
Poole, D. & Mackworth, A.; Artificial Intelligence: Foundations of Computational Agents

Teaching methods and learning activities

Theoretical classes: exposition with interaction. Theoretical-practical classes: modeling, problem-solving, programming exercises and project development.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	50,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	60,00
Estudo autónomo	60,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

To be admitted in the assessment process, students must reach markings >=7.5 in the continuous assessment (CA) components.

Calculation formula of final grade

There will be a test and two practical assignments (to be carried out in groups of max. 3 students)


Final Mark = 0.5 x Test+ 0.25 x Assignment1 + 0.25 x Assignment2

Examinations or Special Assignments

Internship work/project

Special assessment (TE, DA, ...)

The same evaluation criteria is used for all students.

Classification improvement

Project evaluations cannot be improved.

Observations