Advanced Topics on Artificial Intelligence

Code:

CC4022

Acronym:

CC4022

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2014/2015 - 2S (of 16-02-2015 to 06-06-2015)

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	8	Study plan since 2014/2015	1	-	6	42	162
MI:ERS	5	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".

Learning outcomes and competences

It is expected that the students acquire a better knowledge 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

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