Artificial Intelligence

Code:

CC2006

Acronym:

CC2006

Level:

200

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2020/2021 - 2S

Active?	Yes
Web Page:	http://piazza.com/up.pt/spring2021/cc2006
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Computer Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:B	0	Official Study Plan	3	-	6	56	162
L:CC	97	Plano de estudos a partir de 2014	2	-	6	56	162
L:EG	0	The study plan from 2019	3	-	6	56	162
L:F	3	Official Study Plan	2	-	6	56	162
			3
L:G	1	study plan from 2017/18	2	-	6	56	162
			3
L:M	4	Official Study Plan	2	-	6	56	162
			3
L:Q	0	study plan from 2016/17	3	-	6	56	162
MI:ERS	101	Plano Oficial desde ano letivo 2014	2	-	6	56	162

Teaching language

Suitable for English-speaking students

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

B-learning

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

It is strongly recommended that students have attended at least the following two course units, or equivalent ones: Design and Analysis of Algorithms (CC2001) and Data Structures (CC1007).

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 and Metaheuristics. Iterative improvement algorithms: hill-climbing and random -restart hill-climbing, simulated annealing. Constraint Satisfaction Problems: arc-consistency methods; constraint propagation algorithms. 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. Planning and Intelligent Robotics.

4 . Machine learning: inductive systems. Decision trees, information gain. Data Analysis.

5 . 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
David Poole and Alan Mackworth; Artificial Intelligence: foundations of computational agents, Cambridge University Press, 2017. ISBN: 978-1107195394 (https://artint.info)

Complementary Bibliography

Ivan Bratko; Prolog programming for Artificial Intelligence. ISBN: 0-201-40375-7
Holger H. Hoos; Stochastic local search. ISBN: 978-1-55860-872-6
Christian Blum; Swarm intelligence. ISBN: 9783540740889
Francesca Rossi; Handbook of constraint programming. ISBN: 0-444-52726-5
Pedro Domingos; The Master Algorithm: How the quest for the ultimate learning machine will remake our world, Penguin Books, 2017. ISBN: 978-0-141-97924-3

Teaching methods and learning activities

Regular lectures for exposing the program topics and discussing examples.

Practical classes for problem solving and for developing small projects, employing the algorithms learned in the theoretical classes.

Software

WEKA
YAP ou SWI
Aleph
ECLiPse Prolog

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	65,00
Trabalho prático ou de projeto	35,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	2,00
Estudo autónomo	80,00
Frequência das aulas	56,00
Trabalho escrito	8,00
Trabalho laboratorial	16,00
Total:	162,00

Eligibility for exams

No frequency loss. All students can take the exam.

Calculation formula of final grade

-Practical project  NT (developed in groups), weighted 35% (7 points).

- If the evolution of the COVID'19 pandemic allows, there will be two written tests during the semester (not mandatory): T1 weighted 30% (6 points) and T2 weighted 40% (8 points);

- Students need to have a minimum score of 8 (in 20) in each written tests or in the final exam  (Ex) to be approved.

- The final grade is given by NE*0.65+NT*0.35, with NE=max( (3*TE1 + 4*TE2)/7, Ex). 

Students approved through tests can take the first exam to improve their grade (with no penalty).

===================

Additional remark (February 12, 2021):

Most likely, we will not have tests during the semester. 

Classification improvement

Final exam. The lab assignment grade cannot be improved.

Observations

Previous background in "Design and Analysis of Algorithms" and  "Data Structures".