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Course Module Information
Course Modules
CT475: Machine Learning & Data Mining
Semester 1 | Credits: 5
Definitions of Machine Learning, Data Mining and the relationship between them; the CRISP Data Mining process model; major tasks including classification, regression, clustering, association learning, feature selection, and reinforcement learning; algorithms for these tasks that may include decision tree learning, instance-based learning, probabilistic learning, support vector machines, linear and logistic regression, and Q-learning; open-source software tools for data mining; practical applications such as sensor data analysis, healthcare data analysis, and text mining to identify spam email; ethical issues and emerging trends in data mining and machine learning.
(Language of instruction: English)
Learning Outcomes
- Define Machine Learning and Data Mining and discuss their relationship
- Explain what major categories of Machine Learning tasks entail
- Demonstrate how to apply the Data Mining process to practical problems
- Explain and apply algorithms for decision tree learning, instance-based learning, linear and logistic regression, probabilistic learning, support vector machines, and reinforcement learning
- Given a dataset and data mining task to be addressed, select, apply and evaluate appropriate algorithms, and interpret the results
- Discuss ethical issues and emerging trends in data mining and machine learning.
Assessments
- Written Assessment (75%)
Teachers
- MICHAEL MADDEN:
Research Profile |
Email
Note: Module offerings and details may be subject to change.