Anton Antonov — 45 minutes
In this presentation we discuss the implementations of different Machine Learning (ML) algorithms in Raku.
The main themes of the presentation are:
- ML workflows demonstration
- Software engineering perspective on ML implementations
- ML algorithms utilization with Raku's unique features
Here is a list of the considered ML algorithms:
1. Fundamental data analysis
1.1. Outlier identifiers ("ML::OutlierIdentifiers")
1.2. Cross tabulation ("Data::Reshapers")
1.3. Summarization ("Data::Summarizers")
1.4. Pareto principle adherence
2. Supervised learning
2.1 Classifiers
2.2. Receiver Operating Characteristics (ROCs) ("ML::ROCFunctions")
3. Unsupervised learning
3.1. Clustering ("ML::Clustering")
3.2. Tries with frequencies ("ML::TriesWithFrequencies")
3.3. Streams Blending Recommender (SBR) ("ML::StreamsBlendingRecommender")
3.4. Association Rule Learning (ARL) ("ML::AssociationRuleLearning")
3.5. Regression
3.6 Latent Semantic Analysis (LSA)
(See the corresponding mind-map.)
(The document "Trie based classifiers evaluation" provides an example application of the Raku packages mentioned above.)