The “Machine Learning for Data Streams with Practical Examples in MOA” textbook is a resource intended to help students and practitioners enter the field of machine learning and data mining for data streams. The online version of the book is now complete and will remain available online for free.
This textbook can now be ordered on Amazon.
HTML online version of the book.
Citing the book
To cite this book, please use this bibtex entry:
@book{MOA-Book-2018, title={Machine Learning for Data Streams with Practical Examples in MOA}, author={Bifet, Albert and Gavald\`a, Ricard and Holmes, Geoff and Pfahringer, Bernhard}, publisher={MIT Press}, note={\url{https://moa.cms.waikato.ac.nz/book/}}, year={2018} }
Slides
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- Slides used in courses at Telecom ParisTech by Albert Bifet:
- Lecture 1: Introduction
- Lecture 2: Stream and Sketches
- Lecture 3: Dealing with Change
- Lecture 4: Classification
- Lecture 5: Ensemble Methods
- Lecture 6: Regression
- Lecture 7: Clustering
- Lecture 8: Frequent Pattern Mining
- Slides used in courses at Universitat Politecnica de Catalunya by Ricard Gavaldà:
- Lecture 1: The data stream model. Counting. Probability tools
- Lecture 2. Frequency problems
- Lecture 3. Sampling. Finding frequent elements. The CM-sketch
- Lecture 4. Distributed sketching – Graphs Streams.
- Lecture 5. Linear algebra, dimensionality reduction
- Lecture 6. Managing time change in data streams
- Lecture 7. Data Stream Mining. Building decision trees
- Lecture 8. Evaluation. More predictors. Clustering
- Lecture 9. Frequent pattern mining in data streams (see also Albert Bifet’s slides)
- Slides used in courses at Telecom ParisTech by Albert Bifet: