15th Feb 2017 - TRUG Michal Poczwardowski [email protected] dmp @ 3cityIT slack 

f Machine learning - field of study that gives computers the ability to learn without being explicitly programmed. Arthur Samuel, 1959 

gathered data determines output supervised vs unsupervised 

● Recommendations ● Expert systems ● Spam filters ● Classifications ● Google Search 

● Decision Tree - ID3 ● Jaccard Index ● K-means clustering ● Naive Bayes ● Artificial Neural Networks ● + more more more 

? ● Complicated written using unknown symbols might be discouraging ● Use prepared tools / libraries 

? ● lack of stuff ● speed (historical) ● old gems / not so many resources ● ML and AI experts mosty use Java and Python, Ruby hasn't got much love - most of them come from Universities and more scientific studies - Ruby is just not used in such environments 

● github.com/igrigorik/decisiontree ● github.com/reddavis/K-Means ● github.com/francois/jaccard ● github.com/davidcelis/recommendable (for models) 

#1 1: require 'decisiontree' 2: 3: attributes = ['Temperature'] 4: training = [ 5: [36.6, 'healthy'], 6: [37, 'sick'], 7: [38, 'sick'], 8: [36.7, 'healthy'], 9: [40, 'sick'], 10: [50, 'really sick'], 11: ] 

#2 12: dec_tree = DecisionTree::ID3Tree.new(attributes, training, 'sick', :continuous) 13: dec_tree.train 14: 15: test = [37.5, 'sick'] 16: decision = dec_tree.predict(test) 17: puts "Predicted: #{decision} ... True decision: #{test.last}" # => Predicted: sick ... True decision: sick 

1: a = ["likes:jeans", "likes:blue"] 2: b = ["likes:jeans", "likes:women", "likes:red"] 3: c = ["likes:women", "likes:red"] 4: 5: # Determines how similar a pair of sets are 6: Jaccard.coefficient(a, b) 7: #=> 0.25 8: 9: Jaccard.coefficient(a, c) 10: #=> 0.0 11: 12: Jaccard.coefficient(b, c) 13: #=> 0.6666666666666666 14: 15: # According to the input data, b and c have the most similar likes. 

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- 1: require 'k_means' 2: 3: data = [[1,1], [1,2], [1,1], [1000, 1000], [500, 500]] 4: kmeans = KMeans.new(data, centroids: 2) 5: kmeans.inspect 6: => [[3, 4], [0, 1, 2]] 

- f : ● K-means exaple HERE ● 

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● https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer ● http://otobrglez.opalab.com/ruby/2014/03/23/simple-ruby-recommendation-system.html ● https://en.wikipedia.org/wiki/Jaccard_index ● https://en.wikipedia.org/wiki/K-means_clustering ● https://en.wikipedia.org/wiki/ID3_algorithm 

thanks!