Teachers and students. UCLA is offering 3 courses in causal inference. The one in computer science is taught by Prof. Adnan Darwiche
Here is a recording of his first two lectures:
—Part A: https://t.co/Lk1bxkB08Z
—Part B: https://t.co/CRHzmyDoer
Enjoy.
We're hiring! I'm looking for data scientists and software engineers to join my team and to help us build out both our computer vision and chemical machine learning platforms. We also have an opening for a senior scientist. See https://t.co/fCbBAIPSjj for details and to apply.
We are hiring Staff Engineers! (backend, frontend, mobile)
Our notion of Staff Eng is heavily inspired by @Lethain's work on it. Specifically:
Please refer anyone you know, RT, or DM me to learn more, or just apply here: https://t.co/RkQtBt43Hu
@ScopeKurt I tried to read this but I didn’t have a NYTimes account. So I created one and then it said I had already reached my limit of free stories for this month. RIP
¿No les ha pasado que en entrevista te preguntan por la complejidad de tu solución y te hablan sobre "Big O Notation"?
Bueno aquí les dejo una clase rápida de "Big O Notation"
My job is offering an 11-week internship program for anyone trying to get into Cybersecurity, especially if you do not meet the traditional background requirements. It’s fully virtual at $28/hr at no cost to you. Please reach out!
This coming Tuesday at 2-3:30PM PST @seanjtaylor and I will be presenting (in English) at the Nubank Data Science & Machine Learning Meetup, talking about causal identification to a data science/ML audience. If you're interested you can sign up here https://t.co/RU1iJE1Tei
Please RT! We have an online workshop "Causal inference from longitudinal data" from Mar 29-Apr 1. Highly interdisciplinary session with experts in neuroscience, psychology, political science, epidemiology & philosophy. Participation is free.
https://t.co/cevEka9CCs
(1/4)
I think a lot of people underestimate the liberal arts / philosophy value of Math, CS and Stats concepts.
* Taylor Series (things don't have to be perfect to work)
* Bandits/RL (earn/learn trade-off)
* Confidence intervals (uncertainty drops with more data)
"OLS of Y on X is unbiased only if X is unrelated to the error e. If there's a term Z related to X in e, the sign of the bias is the product of the signs of Cov(X,Z) and Cov(Y, Z)"
???
"If Z hangs around X but OLS doesn't know about it, it'll give X all the credit for Z"
"oh ok"
Let's put this in its proper scale and you'll see that it's an enormous effect: 70 minutes per day = nearly 6 hours per week. A typical work week is 40 hours, so we're talking about effective savings of 15% of the work week! HUGE GAINS.
One of the first memes I share with students. I ask "Where are you in this picture? Are you in the box of memorizing formulas or are you in the play area of math exploration?" At the end of the course, I show this meme and ask the same question.