Simple Probabilistic Inference in a Manufacturing Context

How does one learn from data? With the explosion of data in so many business contexts, data science is no longer an optional discipline. With advanced statistical methods packaged in pretty libraries and commoditized for machine learning, it can be all too easy to miss the foundations of probability theory that is at the heart of how we learn from data. Those foundations are over 250 years old, and are both intuitive and philosophical. Understanding these foundations helps us be better practitioners of data science, machine learning and experimentation. To do this, I am going to draw on a workshop I recently taught titled “The Magic of Probability” (public domain bilingual slides in English and Kannada <a href="https://docs.google.com/presentation/d/1263bvlP0UJbn1aEMTpVZNrTrK6AxCHXo/edit?usp=sharing&ouid=104313030205366940545&rtpof=true&sd=true" rel="noopener ugc nofollow" target="_blank">here</a>) under the auspices of the Dr. R. Venkatram Memorial Lecture Series at Bangalore Institute of Technology, my alma mater. The participants were mostly senior faculty members across all disciplines of engineering. As part of a deep dive, I asked for someone to volunteer a problem of inference. <a href="https://towardsdatascience.com/simple-probabilistic-inference-in-a-manufacturing-context-b0f22f7b9a9a">Visit Now</a>