Study-Unit Description

Study-Unit Description


CODE DOC6042

 
TITLE Machine Learning with Python

 
LEVEL D - Doctoral Workshops/Symposium

 
ECTS CREDITS Not Applicable

 
DEPARTMENT Doctoral School

 
DESCRIPTION Machine learning is the science of training algorithms to learn from data without the use of rules or analytic approaches. The vast amount of data gathered from sources varying from sensors and instrumentation to healthcare, social media and finance often require automated processing in a timely manner to extract information useful for decision-making. In this workshop, doctoral researchers will get the opportunity to “look under the hood” and understand the basics of typical machine learning algorithms related to regression, classification and clustering using Python.

Outcomes:

By the end of this workshop, doctoral researchers should be able to:

- fit a model to data using regression techniques;
- understand the basic mathematical foundations of supervised and unsupervised machine learning algorithms;
- train a neural network to perform classification;
- use unsupervised learning techniques to cluster data;
- use Python machine learning libraries, such as keras and scikit-learn.

 
ADDITIONAL NOTES DOC6024 or proven experience in Python programming is a prerequisite for this workshop.

This workshop is split into two parts – Part 1 and Part 2. Attendance is required in both parts.

Timetable Details

Semester 1
Occ A - Part 1 - Date: 11 Dec 2019; Time: 13:00 - 16:00; Duration: 3 hours
Occ A - Part 2 - Date: 18 Dec 2019; Time: 13:00 - 16:00; Duration: 3 hours

 
STUDY-UNIT TYPE Workshop

 
METHOD OF ASSESSMENT
Assessment Component/s Resit Availability Weighting
Attendance No 100%

 
LECTURER/S Jean Paul Ebejer
Reuben Farrugia
Kenneth M. Scerri
Gianluca Valentino

 
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Units not attracting a sufficient number of registrations may be withdrawn without notice.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 2019/0, if study-unit is available during this academic year, and may be subject to change in subsequent years.

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