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The symbolic AI community is increasingly trying to embrace machine learningin neuro-symbolic architectures, yet is still struggling due to culturalbarriers. To break the barrier, this rather opinionated personal memo attemptsto explain and rectify the conventions in Statistics, Machine Learning, andDeep Learning from the viewpoint of outsiders. It provides a step-by-stepprotocol for designing a machine learning system that satisfies a minimumtheoretical guarantee necessary for being taken seriously by the symbolic AIcommunity, i.e., it discusses "in what condition we can stop worrying andaccept statistical machine learning." Unlike most textbooks which are writtenfor students trying to specialize in Stat/ML/DL and willing to accept jargons,this memo is written for experienced symbolic researchers that hear a lot ofbuzz but are still uncertain and skeptical. Information on Stat/ML/DL iscurrently too scattered or too noisy to invest in. This memo prioritizescompactness, citations to old papers (many in early 20th century), and conceptsthat resonate well with symbolic paradigms in order to offer time savings. Itprioritizes general mathematical modeling and does not discuss any specificfunction approximator, such as neural networks (NNs), SVMs, decision trees,etc. Finally, it is open to corrections. Consider this memo as somethingsimilar to a blog post taking the form of a paper on Arxiv.

Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics