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(Machine Learning Foundations)—Mathematical Foundations

，副教授 (Associate Professor)，资讯工程学系 (Computer Science and Information Engineering)

Occam’s Razor

• entities must not be multiplied beyond necessity
• trim down unnecessary explanation
• simple: small hypothesis/model complexity
• direct action: linear first; always ask whether data over-modeled
• simple is good

Sampling Bias

• If the data is sampled in a biased way, learning will produce a similarly biased outcome.
• match test scenario as much as possible 测试的环境和训练的环境尽可能接近

Data Snooping

• your brain’s ‘model complexity’
• data snooping by shift-scale all values
• later author snooped data by reading earlier papers
• 

Power of Three