Today’s Fact: Linear Discriminant Analysis
Linear Discriminant Analysis, or LDA for short, is an ML model used in classification. Specifically, for a model of
ntraining points, it assumes that the
dfeatures are all distributed Normally (Gaussian), with the same variance. More formally, taken from my professor’s cs189 notes, we get:
Today’s Fact: Temperature, Defined Statistically
Today’s Fact: Market for Lemons
Source: Stat 155 Textbook, Game Theory by Anna R. Karlin and Yuval Peres
For the past semester, I have deployed a custom shortlinker service and my personal website on a Google Cloud Compute Free Instance. The custom shortlinker is a rewrite of the CS61A Shortlinker in Elixir, which was a good way for me to learn the new language. Upon deploying the Elixir version, however, I noticed that CPU usage hovered around a constant 100%, even the system was idling. I left it alone for a while, noting that it was responding to requests fairly quickly, leaving me to think that it was likely the BEAM that was idling.
Hello! This is a talk I gave at EE375, an introduction to teaching techniques class required at UC Berkeley for first-time TAs. The content is largely based on James Hoffman’s techniques, with inspiration from other famous figures. Since the talk was limited to 3 minutes, I skipped a lot of (quite lengthy) discussions, including coffee grind sizes, water ratios, and water temperature. If you have any questions about the material, feel free to email me!