Study-Unit Description

Study-Unit Description


CODE CGS5081

 
TITLE Artificial Intelligence

 
UM LEVEL 05 - Postgraduate Modular Diploma or Degree Course

 
MQF LEVEL 7

 
ECTS CREDITS 5

 
DEPARTMENT Cognitive Science

 
DESCRIPTION One way to understand a human cognitive system is to try to build an artificial model that duplicates some of its characteristics. Such a model can, under certain circumstances, have explanatory power. The field of Artificial Intelligence (AI) is targeted particularly at building computational models of those characteristics that constitute intelligence. Opinions differ on the nature of those characteristics and on the techniques used to model them.

This study-unit introduces fundamental concepts in AI from a theoretical and cognitive perspective. We will discuss questions concerning what "intelligence" is and whether machines can have intelligent properties.

Students will be introduced to a variety of typical AI problems in such areas as perception, reasoning, creativity, (machine) learning and language processing.

The study-unit will also relate AI to the field of cognitive modelling where the task is not necessarily to solve a real-world problem, but to create a computational simulation of a limited but nevertheless significant part of the problem.

The study-unit will be run as a three week intensive module:
Week 1: Reading;
Week 2-3: Face-to-face lectures and practical workshops;
Week 3 onwards: Case study: critique of an approach to an AI problem, or practical solution to an AI problem, presented in a report.

Study-unit Aims:

The aims of this study-unit are:

- to expose students to how AI relates to the broader scope of Cognitive Science by considering how AI can contribute to understanding natural intelligence;
- to provide concrete examples of AI problems and solution techniques.

Learning Outcomes:

1. Knowledge & Understanding
By the end of the study-unit the student will be able to:

- explain essential characteristics of AI problems in the context of the history of the field;
- critique approaches to machine intelligence, including models of creativity, language processing and learning;
- see whether a given AI approach is knowledge and data driven approaches.

2. Skills
By the end of the study-unit the student will be able to:

- see the relation between different AI techniques and different schools of thought in cognitive science (e.g., empiricist versus nativist positions);
- run basic machine-learning experiments to solve data-driven problems.

Main Text/s and any supplementary readings:

- Poole, D. and Mackworth, A. (2010). “Artificial Intelligence: Foundations of Computational Agents”. Cambridge University Press. Available on-line at http://artint.info/html/ArtInt.html.
- Russel, S. and Norvig, P. (2009). “Artificial Intelligence: A Modern Approach”. Prentice Hall. 3rd Edition.
- H. Levesque (2017). Common sense, the Turing test and the quest for real AI. MIT Press.

 
STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Sept. Asst Session Weighting
Case Study (Take Home) SEM2 Yes 100%

 
LECTURER/S Vanessa Camilleri
Matthew Montebello

 

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years.

https://www.um.edu.mt/course/studyunit