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Certificate of Achievement
has completed the following course:
The University of Waikato
This online course explored practical data mining – methods for turning raw data into useful information. It explained the principles of many algorithms and how to use them in applications. Participants learned how to use the Weka workbench to mine their own data with state-of-the-art techniques.
5 weeks, 3 hours per week
Ian H. Witten
Professor of Computer Science
The University of Waikato
Transcript
Learning outcomes
- Demonstrate use of Weka for key data mining tasks
- Evaluate the performance of a classifier on new, unseen, instances
- Explain how data miners can unwittingly overestimate the performance of their system
- Identify learning methods that are based on different flavors of simplicity
- Apply many different learning methods to a dataset of your choice
- Interpret the output produced by classification methods
- Describe the principles behind many modern machine learning methods
- Compare the decision boundaries produced by different classification algorithms
- Debate ethical issues raised by mining personal data
Syllabus
- What is data mining?
- Where can it be applied?
- How do simple classification algorithms work?
- What are their strengths and weaknesses?
- In what ways are real-life classification methods more complex?
- How should you evaluate a classifier’s performance?
- What is “overfitting” and how can you combat it?
- How can ensemble techniques combine the result of different algorithms?
- What ethical considerations arise when mining data?
Issued on 27th November 2017
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