Advanced Topics on Artificial Intelligence
Keywords |
Classification |
Keyword |
OFICIAL |
Computer Science |
Instance: 2020/2021 - 2S ![Requerida a integração com o Moodle Ícone do Moodle](/fcup/pt/imagens/MoodleIcon)
Cycles of Study/Courses
Teaching language
Portuguese and english
Obs.: English (classes will be recorded in English)
Objectives
Provide students with knowledge about new AI developments that involve advances in areas as diverse as logic, statistics and operations research.
Emphasis will be placed on:
- directed and non-directed probabilistic systems, including inference and learning of parameters and structure; connection to linear classifiers and neural networks
- logical representation: First order logic (FOL) and Datalog for structure representation; learning logical programs in Inductive Logic Programming (ILP).
- integration: Statistical relational learning (SRL) and neural-logical networks.
The course requires skills acquired in Design and Analysis of Algorithms, Artificial Intelligence and Data Mining.Learning outcomes and competences
Students will develop competences on the usage of artificial intelligence and search / optimization methods in practical situations, in which a part of the knowledge is available in data sets.
Working method
Presencial
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
Algorithm Design and Analysis, Artificial Intelligence, Data Mining I
Program
1. Review of the main concepts in artificial intelligence
2. (Probabilistic) Graphical Models
3. Knowledge-based decisions systems
4. Algorithms for search and optimization
5. Learning
Mandatory literature
Kevin P. Murphy;
Machine learning. ISBN: 978-0-262-01802-9
Battiti Roberto;
The LION way. ISBN: 9781496034021
Complementary Bibliography
Hastie Trevor;
The elements of statistical learning. ISBN: 9780387848570
Wolsey Laurence A.;
Integer programming. ISBN: 9780471283669
Haykin Simon S. 1931;
Neural networks. ISBN: 9780132733502
Russell Stuart J. (Stuart Jonathan);
Artificial intelligence. ISBN: 9780132071482 pbk
Comments from the literature
Online:
- Machine learning: An Introduction to Statistical Learning. https://www.statlearning.com
- Reinforcement Learning: An Introduction. Richard S. Sutton and Andrew G. Barto.
Teaching methods and learning activities
* Lectures: presentation of the program topics and discussion of examples.
* Project: two homeworks to be developed by the students, iin groups of at most 3
The lecturers will propose a number of challenges, but the students may choose a task,
Evaluation: a report, code, and a presentation.
keywords
Physical sciences > Mathematics > Applied mathematics > Operations research
Physical sciences > Mathematics > Algorithms
Physical sciences > Computer science > Cybernetics > Artificial intelligence
Evaluation Type
Distributed evaluation with final exam
Assessment Components
designation |
Weight (%) |
Exame |
50,00 |
Trabalho escrito |
50,00 |
Total: |
100,00 |
Amount of time allocated to each course unit
designation |
Time (hours) |
Elaboração de relatório/dissertação/tese |
40,00 |
Estudo autónomo |
80,00 |
Frequência das aulas |
42,00 |
Total: |
162,00 |
Eligibility for exams
* Submitting the requested assignments, and obtaining a grade of 50% or more.
Calculation formula of final grade
* Tests and exam have a minimum grade, at least 30%
0.50 * grade at exam + 0.50 * grade at assignments
If the student has a positive grade with the tests and assignments (and minimum grade in the tests) he/she will be excused from taking the exam. In this case, the student can take the exam as an improvement.
Students who are unable or unwilling to take one or both tests, can go to the exam to complete the missing component.
The exam will be in the "normal" season and divided into two parts, one corresponding to the subject of the first test and the other corresponding to the subject of the second test.Classification improvement
Final examination