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) -Ron Meir +Jupyter/notebook +Probability -Lexical feature selection"

Positive matches

• 0.125 + - Class imbalance
• 0.032 + - Recommender system
• 0.029 + - Gradient descent
• 0.026 + - IPython notebook
• 0.024 + - Ulrike von Luxburg
• 0.024 + - Data clustering
• 0.024 + - scikit-learn
• 0.022 + - Probability theory
• 0.022 + - Probability distribution
• 0.021 + - Poker
• 0.018 + - Yoav Freund
• 0.018 + - Rob Schapire
• 0.015 + - Privacy and Deanonymization
• 0.014 + - Artemy Kolchinsky
• 0.014 + - Learning
• 0.014 + - IPython
• 0.014 + - Jupyter
• 0.013 + - tfidf
• 0.013 + - Text analysis
• 0.012 + - Gensim

Negative matches

• 0.021 + - Data analysis
• 0.012 + - Natural language processing
• 0.005 + - Biomedical natural language processing
• 0.004 + - NLP
• 0.003 + - CNN
• 0.003 + - StanfordNLP
• 0.002 + - Convolutional neural network
• 0.001 + - String metric
• 0.001 + - Word segmentation
• 0.001 + - scispaCy
• 0.001 + - KenLM
• 0.001 + - Latent Dirichlet allocation
• 0.001 + - KoNLPy
• 0.001 + - BookNLP
• 0.001 + - fastText
• 0.001 + - Sentiment analysis
• 0.001 + - Dialog act
• 0.001 + - David Feldman
• 0.001 + - Information Theory, Inference, and Learning Algorithms
• 0.001 + - Pointwise mutual information