| CODE | DOC6042 | ||||||
| TITLE | Machine Learning with Python | ||||||
| UM LEVEL | D - Doctoral Workshops/Symposium | ||||||
| MQF LEVEL | Not Applicable | ||||||
| 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. |
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| RULES/CONDITIONS | Before OR WHILE TAKING THIS UNIT YOU MUST TAKE DOC1024 OR TAKE DOC6024 | ||||||
| ADDITIONAL NOTES | This workshop is split into two parts – Part 1 and Part 2. Attendance is required in both parts. Timetable Details - Please click here for further details. |
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| STUDY-UNIT TYPE | Workshop | ||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Matthew Montebello |
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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 Units not attracting a sufficient number of registrations may be withdrawn without notice.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. |
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