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 -EM algorithm -Ulrike von Luxburg +scikit-learn -Neuron"

Positive matches

• 0.114 + - Class imbalance
• 0.028 + - Recommender system
• 0.026 + - Decision tree
• 0.015 + - Random forest
• 0.014 + - tfidf
• 0.013 + - Learning
• 0.013 + - Python/Modules
• 0.013 + - tf-idf
• 0.013 + - Privacy and Deanonymization
• 0.011 + - Poker
• 0.011 + - Jupyter/notebook
• 0.011 + - skorch
• 0.011 + - IPython notebook
• 0.011 + - Boosting (machine learning)
• 0.010 + - Deep learning
• 0.010 + - Tim Hopper
• 0.010 + - Python
• 0.009 + - Future of work
• 0.009 + - Regular expression
• 0.009 + - Lexical feature selection

Negative matches

• 0.032 + - Neural network
• 0.001 + - Gender bias
• 0.001 + - sense2vec
• 0.001 + - Paper/Levy2014a
• 0.001 + - Clinical concept embedding
• 0.001 + - Long short-term memory
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Chris McCormick
• 0.001 + - Wolfgang Maass
• 0.000 + - Alex Graves
• 0.000 + - Paper/Hochreiter1997
• 0.000 + - Paper/Bahdanau2015
• 0.000 + - Rami Al-Rfou
• 0.000 + - Ivo Danihelka
• 0.000 + - KoNLPy
• 0.000 + - Sentiment analysis
• 0.000 + - Paper
• 0.000 + - Topic modeling
• 0.000 + - Jack Terwilliger
• 0.000 + - Neural Turing machine