MA5832 - Data Mining and Machine Learning
| Credit points: | 03 |
| Year: | 2019 |
| Student Contribution Band: | Band 2 |
| Administered by: |
This subject will provide students with a range of algorithms based on machine learning techniques for advanced data analysis and mining. These algorithms and techniques fall within the most common machine learning paradigms, namely, unsupervised, semi-supervised, and supervised learning. In particular, students will learn sophisticate machine learning methods for clustering, outlier detection, classification, feature selection, and regression.
Learning Outcomes
- explain what machine learning for data mining is about and identify the most common tasks and roles of machine learning in the realm of data mining;
- describe, choose, and apply unsupervised machine learning methods for descriptive data mining tasks, such as clustering and outlier detection;
- describe, choose, and apply supervised techniques for dimensionality reduction via feature selection;
- describe, choose, and apply semi-supervised and/or supervised machine learning methods for predictive data mining tasks, such as pattern classification and regression.
| Prerequisites: | MA5810 AND 24CP OF POSTGRADUATE SUBJECTS |
Availabilities | |
| , Study Period 85 | |
| Census Date 12-Sep-2019 | |
| Contact hours: |
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| Method of Delivery: | Online - JCU |
| Assessment: | quizzes or tests (20%); assignments (60%); computational laboratories/log book (20%). |
Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.