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 +tf-idf +Boosting (machine learning) +Recommender system +Ron Meir +Ulrike von Luxburg -Yoav Freund +Lexical feature selection -Gensim"

Positive matches

• 0.125 + - Class imbalance
• 0.034 + - Gradient descent
• 0.032 + - Text analysis
• 0.031 + - Netflix prize
• 0.030 + - Pointwise mutual information
• 0.028 + - Data analysis
• 0.022 + - scikit-learn
• 0.021 + - Data clustering
• 0.019 + - Learning
• 0.018 + - Natural language processing
• 0.016 + - Regular expression
• 0.016 + - Rob Schapire
• 0.013 + - NLTK
• 0.011 + - Jupyter/notebook
• 0.010 + - Poker
• 0.010 + - IPython notebook
• 0.009 + - Privacy and Deanonymization
• 0.009 + - Neural network
• 0.009 + - Regex
• 0.008 + - Future of work

Negative matches

• 0.027 + - word2vec
• 0.005 + - tfidf
• 0.004 + - Hierarchical softmax
• 0.002 + - sense2vec
• 0.001 + - Continuous embedding
• 0.001 + - Network geometry
• 0.001 + - Graph embedding
• 0.001 + - Clinical concept embedding
• 0.001 + - WordRank
• 0.001 + - Softmax function
• 0.001 + - Gender bias
• 0.001 + - GloVe
• 0.001 + - Paper/Levy2014
• 0.001 + - Google
• 0.001 + - Ilya Sutskever
• 0.001 + - Omer Levy
• 0.001 + - Finkbeiner test
• 0.000 + - Chris McCormick
• 0.000 + - Dan Jurafsky
• 0.000 + - Manic Pixie Dream Girl