Study-Unit Description

Study-Unit Description


TITLE Applied Machine Learning

LEVEL 05 - Postgraduate Modular Diploma or Degree Course


DEPARTMENT Artificial Intelligence

DESCRIPTION This study-unit focuses on advanced machine learning techniques and data analysis, with emphasis on the following topics:

- Extracting statistical information and structure from data
- Forecasting and decisions
- Visualisation
- Probabilistic modelling
- Introduction to tools such as Spark and R
- Classification and clustering
- Advanced neural networks
- Practical optimization problems

Study-unit Aims:

The aims of this study-unit are to:

- instill a deeper understanding and appreciation of advanced techniques employed to analyse data, build predictive models, and build computer systems that perform intelligent decisions;
- investigate the techniques involved in distinguishing different machine learning techniques that are best suited to deal with real world problems;
- pursue further investigation in how to extract statistical data to build models, identify patterns, and visualise data.

Learning Outcomes:

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

- Explain the principles of data collection and preparation;
- Describe how to identify the correct machine learning techniques for an application;
- Compare advanced machine learning concepts like decision trees, artificial neural networks, support vector machines, and Bayesian inference methods;
- Report on construct systems that learn from, and are driven by data.

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

- Prepare, examine, and visualise data for analysis;
- Apply the methods learnt to real world examples;
- Gain familiarity with tools such as R, Spark, and Hadoop.

Main Text/s and any supplementary readings:

Machine Learning: Hands-On for Developers and Technical Professionals, Jason Bell, ISBN 978-1-118-88906-0
Machine Learning with R, Brett Lantz, ISBN 978-1782162148


STUDY-UNIT TYPE Lecture & Independent Online Learning

Assessment Component/s Resit Availability Weighting
Seminar Paper Yes 10%
Presentation No 10%
Project Yes 80%

LECTURER/S George Azzopardi (Co-ord.)
Claudia Borg
Jean Paul Ebejer
Adrian F. Muscat
Marc Tanti

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.