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CODE ARI5902

 
TITLE Research Topics in Artificial Intelligence

 
LEVEL 05 - Postgraduate Modular Diploma or Degree Course

 
ECTS CREDITS 5

 
DEPARTMENT Artificial Intelligence

 
DESCRIPTION This study-unit exposes students to current relevant research in Artificial Intelligence and should serve to provide them with an awareness of the context in which they will eventually produce a dissertation. Students will be provided with a seminal seed paper in the topic and then guided in systematically exploring a particular area within system software. Topics of interest will include topics such as:

- Creative Technologies;
- Big Data;
- Internet of Things;
- Deep Learning;
- Computer Vision;
- Robotics;
- Human Language Technologies.

To assist students with their reading, meetings are held with the lecturer to discuss the progress and any problems encountered. Once the student has read sufficiently, he or she is expected to draw up a report which reviews the texts under consideration. The review is expected to offer a mature discussion, comparing and contrasting the works within a sensible framework.

Study-Unit Aims:

The aim of this study-unit is to expose the students to state of the art research papers in the area of Artificial Intelligence, help them organise their reading and research efforts whilst also giving them the opportunity to write a literature review and present their findings in a seminar. This will give the students invaluable background in the topic of system software while also giving them the opportunity of hands on research under close guidance from the lecturer.

Learning Outcomes:

1. Knowledge & Understanding:

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

- Understanding the different developments in the field of study together with its practical applications, the current state-of-the-art and future research directions.
- Given a practical problem in Artificial Intelligence, point out and discuss current state-of-the-art research topics which could contribute to the solution of that problem.

2. Skills:

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

- Discuss and critically analyse the motivation, techniques and results in the current software research, with specialisations in Artificial Intelligence.
- Given a seed paper, use it to explore a subject area in depth within a particular context.

Main Text/s and any supplementary readings:

Main Texts:

- Vaishnavi, V. K., and Kuechler, W., Jr., 2007, Design Science Research - Methods and Patterns: Innovating Information and Communication Technology. Auerbach Publications. ISBN: 1420059327
- Machine Learning with R, Brett Lantz. ISBN 978-1782162148
- Han, J., Kamber, M., Pei, J. (2011) Data Mining: Concepts and Techniques, 3rd Edition, The Morgan Kaufmann Series in Data Management Systems). ISBN-13: 978-0123814791

Supplementary Readings:

- Proceeding of the International Conference on Intelligent User Interfaces.
- Proceedings of the Annual Meeting of the Association for Computational Linguistics.
- Proceeding of the ACM Conference on Intelligent User Interfaces.
- International Joint Conference on AI 2009, Workshop on Intelligence and Interaction.
- AI Magazine, Special Issue on Usable AI (2009).
- Interaction Design: Beyond Human - Computer Interaction (2011) - ISBN: 0470665769.
- Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability (2014) - ISBN: 0321965515.
- Machine Learning: Hands-On for Developers and Technical Professionals, Jason Bell - ISBN 978-1-118-88906-0.
- Leskovex, J., Leskovec, J., Rajaraman, A., Ullman, J. (2014) Mining of Massive Datasets, Cambridge University Press.
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman. Cambridge University Press, 2014.
- Hadoop in Practice, Alex Holmes (Manning 2012).
- Hadoop: the Definitive Guide (2nd Edition), Tom White (O'Reilly 2011).
- Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer (Morgan and Claypool 2010).
- Chambers, J.M. (2008) Software for Data Analysis: Programming with R (Statistics and Computing), New York, Springer-Verlag.
- James, G. (2009) An Introduction to Statistical Learning: With Applications in R. New York. Springer-Verlag.

 
STUDY-UNIT TYPE Lecture, Independent Study, Project and Seminar

 
METHOD OF ASSESSMENT
Assessment Component/s Resit Availability Weighting
Project Yes 100%

 
LECTURER/S Charles Abela
Joel Azzopardi
Claudia Borg
Vanessa Camilleri
Alexiei Dingli
Jean Paul Ebejer
Kristian Guillaumier
Dylan Seychell
Christopher D. Staff (Co-ord.)
Vincent Vella

 
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 study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.
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