Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Artificial neural network +Data clustering +Boosting (machine learning) +Ron Meir -Andrew Ng +scikit-learn -tf-idf"

Positive matches

• 0.195 + - Machine learning
• 0.131 + - Class imbalance
• 0.032 + - Recommender system
• 0.031 + - Gradient descent
• 0.024 + - Ulrike von Luxburg
• 0.024 + - Brain network
• 0.021 + - Decision tree
• 0.018 + - Yoav Freund
• 0.018 + - Rob Schapire
• 0.015 + - Random forest
• 0.013 + - Learning
• 0.013 + - Jupyter/notebook
• 0.012 + - IPython notebook
• 0.012 + - Poker
• 0.012 + - Python/Modules
• 0.011 + - Python
• 0.011 + - Privacy and Deanonymization
• 0.010 + - skorch
• 0.009 + - Tim Hopper
• 0.008 + - Future of work

Negative matches

• 0.028 + - Text analysis
• 0.014 + - NLTK
• 0.014 + - Gensim
• 0.011 + - Pointwise mutual information
• 0.009 + - Lexical feature selection
• 0.008 + - Natural language processing
• 0.007 + - Deep learning
• 0.003 + - Data analysis
• 0.002 + - Probability
• 0.001 + - Biomedical natural language processing
• 0.001 + - sense2vec
• 0.001 + - Skip-gram
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Paper/Levy2014
• 0.001 + - Convolutional neural network
• 0.001 + - NLP
• 0.001 + - scispaCy
• 0.001 + - GloVe
• 0.001 + - StanfordNLP
• 0.000 + - Clinical concept embedding