Advanced Topics on Artificial Intelligence

Code:

CC4022

Acronym:

CC4022

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2015/2016 - 2S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~jpp/taia
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Computer Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:CC	4	Study plan since 2014/2015	1	-	6	42	162
MI:ERS	3	Plano Oficial desde ano letivo 2014	4	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

Deepen competences acquired in "Algorithm Design and Analysis" and in "Artificial Intelligence".
Apply optimization and machine learning techniques in decision support.

Learning outcomes and competences

It is expected that the students acquire a better understanding of more complex problems in artificial intelligence and become capable of using the right methodology to solve them.

Working method

Presencial

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

Algorithm Design and Analysis, Artificial Intelligence

Program

1. Artificial Intelligence applications: past, present and future.
2. Search and Optimization (meta-heuristics): Genetic Algorithms, Tabu Search, Simulated Annealing, GRASP, Ant colony, particle swarm.
3. Knowledge-based systems. First-order logic for knowledge representation. Uncertainty in rule-based systems. Fuzzy Models. Case based reasoning.
4. Probabilistic models. Bayesian networks: manipulation, construction and inference. Learning bayesian networks: parameters and structure. Diagnosis. Evaluation.
5. Multi-Agent Systems: arquitecture, interaction: coordination and cooperation, negotiation.
6. Machine Learning. Reinforcement learning, integration of ML and optimization methods.
7. Natural Language Processing. Lexical and Syntactic analysis (parsing), interpretations, automatic translation.
8. Intelligent robots: perception, planning and action.

Mandatory literature

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

Complementary Bibliography

Hastie Trevor; The elements of statistical learning. ISBN: 9780387848570

Comments from the literature

Online:
Reinforcement learning: http://webdocs.cs.ualberta.ca/~sutton/book/ebook/

Teaching methods and learning activities

* Lectures: presentation of the program topics and discussion of examples.
* Project development.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	75,00
Trabalho escrito	25,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de relatório/dissertação/tese	25,00
Estudo autónomo	40,00
Frequência das aulas	40,00
Total:	105,00

Eligibility for exams

* Submitting the requested assignments.

Calculation formula of final grade

0.75 * grade at exam + 0.25 * grade at assignments

Classification improvement

Final examination