Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Machine learning +Machine learning for healthcare -Data clustering +Gradient descent +Machine learning for physical sciences +scikit-learn -Recommender system -Neural network"

Positive matches

• 0.111 + - Class imbalance
• 0.024 + - Ulrike von Luxburg
• 0.024 + - Decision tree
• 0.022 + - Neuron
• 0.016 + - tfidf
• 0.015 + - Random forest
• 0.014 + - skorch
• 0.013 + - Boosting (machine learning)
• 0.013 + - Python/Modules
• 0.012 + - Privacy and Deanonymization
• 0.012 + - Jupyter/notebook
• 0.011 + - tf-idf
• 0.011 + - Poker
• 0.011 + - IPython notebook
• 0.011 + - Tim Hopper
• 0.010 + - Lexical feature selection
• 0.010 + - Python
• 0.010 + - Deep learning
• 0.010 + - Regular expression
• 0.009 + - Regex

Negative matches

• 0.032 + - Netflix prize
• 0.022 + - Paper/Srivastava2014
• 0.007 + - Jack Terwilliger
• 0.007 + - Neural Turing machine
• 0.007 + - Recurrent neural network
• 0.007 + - word2vec
• 0.007 + - Ilya Sutskever
• 0.006 + - Liquid state machine
• 0.006 + - Dropout
• 0.004 + - Graph embedding
• 0.003 + - Hierarchical softmax
• 0.003 + - Tomas Mikolov
• 0.002 + - Gensim
• 0.002 + - Long short-term memory
• 0.002 + - sense2vec
• 0.002 + - Wolfgang Maass
• 0.001 + - WordRank
• 0.001 + - Google
• 0.001 + - Attention (Deep learning)
• 0.001 + - wiki2vec