Search "+Peter Norvig +Automated machine learning +Paradox +Travelling salesman problem +Boosting (machine learning) +Machine learning +Information theory +IPython notebook +Probability +David MacKay +Recommender system +Learning -Rob Schapire +Probability distribution +Lexical feature selection -Ron Meir +Data analysis -Natural language processing"

Positive matches

• 0.064 + - Probability, Paradox, and the Reasonable Person Principle
• 0.037 + - Probability theory
• 0.034 + - Class imbalance problem
• 0.033 + - Class imbalance
• 0.029 + - Netflix prize
• 0.028 + - Simpson's paradox
• 0.023 + - Gradient descent
• 0.022 + - Text analysis
• 0.020 + - Jupyter/notebook
• 0.019 + - Books
• 0.019 + - Beta distribution
• 0.019 + - Paper/MacKay1991
• 0.018 + - Pointwise mutual information
• 0.017 + - Deep learning
• 0.016 + - Industry-Academic Partnership
• 0.016 + - Poker
• 0.015 + - Yoav Freund
• 0.015 + - tf-idf
• 0.015 + - Interdisciplinary research
• 0.014 + - How to study

Negative matches

• 0.013 + - CNN
• 0.005 + - RNN
• 0.003 + - Residual Learning
• 0.002 + - Paper/Gatys2015
• 0.002 + - BERT
• 0.002 + - Word embedding
• 0.002 + - scispaCy
• 0.002 + - Topic modeling
• 0.002 + - Latent Dirichlet allocation
• 0.002 + - spaCy
• 0.002 + - Residual leanrning
• 0.002 + - Kyunghyun Cho
• 0.001 + - Attention (Deep learning)
• 0.001 + - Sentiment analysis
• 0.001 + - Word segmentation
• 0.001 + - KoNLPy
• 0.001 + - KenLM
• 0.001 + - Sentence embedding
• 0.001 + - Paper/Hochreiter1997
• 0.001 + - Musical composition