Intelligent Systems

Code:

CC322

Acronym:

CC322

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2013/2014 - 2S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~ines/aulas/1314/SI/SI.html
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Geology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:AST	0	Plano de Estudos a partir de 2008	3	-	5	49	135
L:B	0	Plano de estudos a partir de 2008	3	-	5	49	135
L:CC	17	Plano de estudos de 2008 até 2013/14	2	-	5	49	135
			3
L:F	0	Plano de estudos a partir de 2008	3	-	5	49	135
L:G	3	P.E - estudantes com 1ª matricula anterior a 09/10	3	-	5	49	135
		P.E - estudantes com 1ª matricula em 09/10	3	-	5	49	135
L:M	1	Plano de estudos a partir de 2009	3	-	5	49	135
L:Q	1	Plano de estudos Oficial	3	-	5	49	135
MI:ERS	37	Plano de Estudos a partir de 2007	3	-	5	49	135

Teaching language

Portuguese

Objectives

Objectives: Study fundamental concepts and techniques of general use for Artificial Intelligence.

Learning outcomes and competences

skills:
- Capacity for judicious choice of Artificial Intelligence techniques for use in concrete applications,
- Ability to deploy applications based on these techniques.

Working method

Presencial

Program

1. Search techniques : trees and graphs, search algorithms: depth-first , breadth-first, iterative deepening. Informed search algorithms: greedy search, A* and A* with limited memory. Heuristics. Iterative improvement algorithms: hill-climbing and random -restart hill-climbing, simulated annealing. Constraint Satisfaction Problems: arc-consistency methods. Methods forward checking and lookahead. Adversarial search: minimax and alpha -beta cut.

2 . Knowledge-based systems: representation and manipulation of knowledge, propositional and first order logic, situation calculus. Inference in first order logic: backward chaining and forward chaining, resolution, refutation. Deductive systems.

3 . Machine learning: inductive systems. Decision trees, information gain. The WEKA tool. Data Analysis.

4 . Biologically inspired models: neural networks and genetic algorithms .

Mandatory literature

S. Russell, P. Norvig; Artificial Intelligence: A Modern Approach, 3rd ed, Prentice Hall, 2009
Nils Nilsson; Artifical Intelligence: a new synthesis, Morgan Kaufmann Publishers, 1998. ISBN: 1558604677

Complementary Bibliography

P. Winston; Artificial Intelligence, 3rd edition, Addison-Wesley, 1992

Teaching methods and learning activities

Information available at http://www.dcc.fc.up.pt/~ines/aulas/1314/SI/SI.html

Software

WEKA
Aleph

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	60,00
Trabalho laboratorial	40,00
Total:	100,00

Calculation formula of final grade

2 tests: T1 with weight 25% (5 points) e T2 with weight 35% (7 points).
4 COMPULSORY practical assignments with presentation with weight 40% (8 points - 2 points per assignment).
Students must have a minimum score in the tests (30%: 1.5 points in test T1 and 2.1 points in test T2).
If T1+T2+Trabs >= 9.5 he/she doesn't need to go to the final exam.
Otherwise or if the student does not have a minimum score in either test, will need to go to the final exam.