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 -tf-idf"

Positive matches

• 0.140 + - Class imbalance
• 0.039 + - Neural network
• 0.037 + - Recommender system
• 0.032 + - Neuron
• 0.026 + - Ulrike von Luxburg
• 0.024 + - Decision tree
• 0.019 + - Boosting (machine learning)
• 0.018 + - Random forest
• 0.017 + - Learning
• 0.015 + - PyTorch
• 0.013 + - Privacy and Deanonymization
• 0.013 + - skorch
• 0.013 + - Python/Modules
• 0.013 + - IPython notebook
• 0.013 + - Jupyter/notebook
• 0.012 + - Future of work
• 0.012 + - Poker
• 0.011 + - Python
• 0.010 + - Tim Hopper
• 0.010 + - Regex

Negative matches

• 0.026 + - Text analysis
• 0.014 + - Gensim
• 0.014 + - NLTK
• 0.011 + - Pointwise mutual information
• 0.009 + - Lexical feature selection
• 0.008 + - Natural language processing
• 0.003 + - Data analysis
• 0.001 + - Biomedical natural language processing
• 0.001 + - Probability
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Paper/Levy2014
• 0.001 + - NLP
• 0.001 + - Paper/Levy2014a
• 0.001 + - scispaCy
• 0.001 + - GloVe
• 0.001 + - StanfordNLP
• 0.000 + - Clinical concept embedding
• 0.000 + - word2vec
• 0.000 + - TensorFlow
• 0.000 + - Paper