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) +IPython notebook +NLTK +Ron Meir +Lexical feature selection -Natural language processing"

Positive matches

• 0.118 + - Class imbalance
• 0.038 + - Text analysis
• 0.036 + - Pointwise mutual information
• 0.029 + - tfidf
• 0.026 + - Gradient descent
• 0.024 + - Recommender system
• 0.022 + - scikit-learn
• 0.022 + - Data clustering
• 0.022 + - Ulrike von Luxburg
• 0.022 + - Jupyter/notebook
• 0.020 + - Data analysis
• 0.018 + - Poker
• 0.017 + - Yoav Freund
• 0.017 + - Rob Schapire
• 0.015 + - Learning
• 0.014 + - Gensim
• 0.013 + - SSH tunneling
• 0.013 + - IPython
• 0.013 + - Regular expression
• 0.012 + - Future of work

Negative matches

• 0.011 + - CNN
• 0.009 + - Residual Learning
• 0.004 + - Residual leanrning
• 0.002 + - Probability
• 0.002 + - fastText
• 0.002 + - BERT
• 0.002 + - String metric
• 0.002 + - Kyunghyun Cho
• 0.002 + - Word segmentation
• 0.002 + - Word embedding
• 0.002 + - KoNLPy
• 0.002 + - KenLM
• 0.002 + - RNN
• 0.002 + - Sentence embedding
• 0.002 + - Latent Dirichlet allocation
• 0.001 + - Paper/Gatys2015
• 0.001 + - Christopher M. Danforth
• 0.001 + - Archetype
• 0.001 + - Topic modeling
• 0.001 + - Attention (Deep learning)