| CODE | ICS5210 | ||||||||||||
| TITLE | Applied Machine Learning | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | Not Applicable | ||||||||||||
| ECTS CREDITS | 6 | ||||||||||||
| DEPARTMENT | Intelligent Computer Systems | ||||||||||||
| 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 aim of this study-unit is 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 data collected for analysis; - Demonstrate the ability to examine and visualise the data analysed; - Explain the knowledge acquired to real world examples; - Employ tools such as R, Spark, and Hadoop to machine learning. 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. |
||||||||||||
| ADDITIONAL NOTES | Pre-requisite Study-units: ICS2207 & CSA3220 | ||||||||||||
| STUDY-UNIT TYPE | Lecture & Independent Online Learning | ||||||||||||
| METHOD OF ASSESSMENT |
|
||||||||||||
| LECTURER/S | |||||||||||||
|
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. |
|||||||||||||