Curricular Unit:			Code:
Artificial Intelligence			1126IART
Year:	Level:	Course:	Credits:
1	Master	Computer Systems Engineering (Mobile Computation)	6 ects
Learning Period:		Language of Instruction:	Total Hours:
Spring Semester		Portuguese/English	78
Learning Outcomes of the Curricular Unit:
Upon successful completion of this course unit students should be able to (learning outcomes - LO): LO1-Understand the notion of intelligent agents LO2-formulate problem solving using search agents LO3-apply uninformed, informed search and local search methods LO4-apply evolutionary algorithms LO5-apply search methods in adversarial environments LO6-Experiment Logic Agents and Logic Programming LO7-apply basic learning models: statistics and decision tree construction LO8-evaluate models learned through observations
Syllabus:
1. Introduction to Artificial Intelligence 2. Intelligent Agents 2.1 Agents, Environments and Properties 2.2 Agent Structures 3. Search 3.1 Problem Solving using Search 3.2 Uninformed search 3.3 Informed search 3.4 Search in Complex Environments 3.5 Constraint Satisfaction Problems (CSP) 3.6 Adversarial search and Games 4. Knowledge Representation, Reasoning and Logic 4.1 Knowledge Based Agent 4.2 Representation, Reasoning and Logic 4.3 Propositional, First-order, Modal and Temporal Logic 5. Introduction to Logic Programming 5.1 Clauses, Facts and Rules 5.2 Syntax and Data Types in Prolog 5.3 Unification and Backtracking Mechanisms 6. Machine Learning 6.1 Learning from examples 6.2 Learning and uncertainty 6.3 Neural Networks
Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives:
The syllabus presented are consistent with the learning objectives of the curricular unit since there is a large convergence between the table of contents and the knowledge that the student is supposed to acquire in each of the program topics. The fundamental concepts of artificial intelligence and intelligent agents are presented in the introductory chapters, in the following chapters are presented various methods and techniques of artificial intelligence such as the search and logic agents. It is also presented the area of machine learning. The learning objectives are achieved by supplementing the theoretical concepts with concrete examples and exercises run in lab environment using appropriate software.
Teaching Methodologies (Including Evaluation):
This Course Unit (UC) considers two possible assessment models: M1-Continuous Assessment; or M2-Exam Assessment Assessment results can be: a) Student achieves a positive Final Grade (NF1 >=9.5 values) in continuous assessment. Approves the UC with NF1 b) Student obtains NF1 < 9.5 values. Will be assessed on examination. Exam assessment is independent of continuous assessment. UC Final Grade is NF2 Expected assessment elements: G1. Test 1 G2. Test 2 G3. Practical project(s) and/or HomeWorks G4. Exam M1-Continuous Assessment Model: Practical Score of Continuous Assessment, NPAC = G3 NF1 = (G1 + G2 + 2*NPAC)/4, NPAC >= 9.5 M2-Exam Assessment Model: NF2 = G4
Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes:
The teaching/learning methodology applied in this curricular unit as well as its evaluation system is perfectly aligned with the objectives to be attained by the students at the end of the term. The theoretical concepts are presented, discussed, applied and evaluated in the context of lectures, which guarantees students a solid foundation to understand the challenges facing this area of knowledge. On the other hand, so that the study is not restricted to conceptual models, in the practical lessons are presented concrete case studies and implemented solutions for real problems using appropriate software. This combination guarantees training for students that allows them to meet the scientific goals, essential to a good understanding of the theme, as well as the ability to adapt to technological changes. The evaluation process consists of theoretical tests and practical work also guarantees a correct balance between the efforts dedicated to both components. The objective is to train professionals’ specialized in state-of-the-art techniques and tools but also ensure its ability to follow future developments. In this curriculum unit the problem and concepts of the artificial intelligence are presented and evaluated in theoretical component. These concepts are then applied in the resolution of worksheets and practical work in the context of practical classes.
Reading:
[1] Russell, Stuart ; Norvig, Peter, “Artificial Intelligence: A Modern Approach”, Prentice Hall, 4th edition, 2021 [2] Costa, E.; Simões, A., “Inteligência Artificial - Fundamentos e Aplicações”, Editora FCA, 2ª edição, 2008 [3] Witten, Frank, Hall, Pal, “Data Mining: Practical Machine Learning Tools and Techniques, 4rd Edition”, Morgan Kaufmann, 2017 [4] I. Bratko, "Prolog Programming for Artificial Intelligence, 4th edition", Pearson Education, 2011 [5] Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly, 2019