Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem +Machine learning -Andrew Ng +Carlos Guestrin +Class imbalance +Poker +Python +Deep learning +Residual leanrning +Gradient descent -Tom M. MItchell -Language"

Positive matches

• 0.068 + - Residual Learning
• 0.033 + - Neural network
• 0.032 + - Neuron
• 0.025 + - Paradox
• 0.025 + - Meta learning
• 0.025 + - Probability
• 0.019 + - Pajek
• 0.016 + - IPython notebook
• 0.015 + - Learning
• 0.013 + - Jupyter/notebook
• 0.011 + - Probability, Paradox, and the Reasonable Person Principle
• 0.009 + - Reinforcement learning
• 0.008 + - Artificial neural network
• 0.008 + - Decision tree
• 0.008 + - GraphLab
• 0.007 + - Cross validation
• 0.007 + - Sargur Srihari
• 0.007 + - Cluster analysis
• 0.007 + - Image completion
• 0.007 + - Training dataset

Negative matches

• 0.005 + - Metaphor
• 0.005 + - Linguistic relativity
• 0.005 + - Language detection
• 0.005 + - WALS
• 0.005 + - Language of thought
• 0.005 + - Linguistic network
• 0.005 + - Zipf's law
• 0.005 + - Home
• 0.005 + - Language development
• 0.005 + - Happinness
• 0.005 + - English language
• 0.005 + - Evolution of language
• 0.004 + - Linguistic diversity
• 0.003 + - Machine translation
• 0.002 + - Convolutional Neural Network
• 0.002 + - Power law
• 0.001 + - Urban scaling
• 0.001 + - Allometry
• 0.001 + - Pareto principle
• 0.000 + - StanfordNLP