Hi #EconTwitter! Are you interested in Bayesian #econometrics, as a student🎓or an instructor👨‍🏫? Explore the three comprehensive courses🎯by Hedibert Lopes (Insper) on this topic! They give you access to #Rstats code, data, slides & more. Don't miss this triple treat! 🚀🌟

"Taxicab Geometry, an Adventure in Non-Euclidean Geometry" by Eugene F. Krause.

companies like Facebook record every imaginable interaction their users have with the platform. they log each of your clicks and taps. they keep track of how long your gaze lingered on a post, whether you were on the same WiFi as that woman who might be your friend, which

Teaching some matrix calculus at the moment, and mostly but not fully satisfied with my notes at the moment...found these, they look very good! Gives me some nice ideas to improve my own notes -- Matrix Calculus (for Machine Learning and Beyond) arxiv.org/abs/2501.14787

Our latest book on the mathematical principles of deep learning and intelligence has been released publicly at: ma-lab-berkeley.github.io/deep-represent… It also comes with a customized Chatbot that helps readers study and a Chinese version translated mainly by AI. This is an open-source project.

"Introduction to Machine Learning Systems" - FREE from MIT Press - Authored by Harvard Professor - 2048 Pages

Instead of using shady websites which store PDFs when you want to split, rearrange, convert from PNG, etc Try: github.com/pdfarranger/pd… FOSS with a GUI, on Windows, Linux & Mac. (Just throwing it out in the wild after seeing a friend upload their passport scans on ilovepdf )

Ivan Aivazovsky, The Black Sea at Night, 1879

Just a quick reminder if you are set to teach metrics, data analysis, methods, check out Gabors Data Analysis. Undergrad + applied MA. 42 full case studies. gabors-data-analysis.com/getting-started * 24 full slides shows * all data and code in #Python, #Rstats, and Stata github.com/gabors-data-an…

Great new book! Material available from.the Authors' home page: www2.isye.gatech.edu/~nemirovs/

The manifold tangent classifier is probably one of my favorite pre-AlexNet DL papers, and it was the first convincing result that I ever saw about manifold learning (though there were many great manifold learning papers prior to it). The basic idea is that data lives on low

Bi-yearly Aaron Swartsz' Guerilla Open Access manifesto repost, read it, it's less than a page, put copies of it in every university and library everywhere. archive.org/details/Gueril…

cool method to learn math, physic or exercise heavy topic is the green, yellow, red method™️ in a nutshell it’s a iterative elimination method that focus the theoretical learning on getting exercise done. as time goes on you focus almost exclusively on the hard problem.

Introduction to Machine Learning by J. Deng and R. Fong and V. Ramaswamy Lecture notes: princeton-introml.github.io

🚨Announcing Formal Positions for cutting edge Clinical AI Research in India 🇮🇳 As promised, we are opening up full time positions for Junior Research Fellows and Research Associates at Koita Centre for Digital Health - Ashoka (KCDH-A) (Ashoka University). You will be closely working with me in developing foundational models

MIT's Optimization Methods by Prof. Dimitris Bertsimas Lecture notes: ocw.mit.edu/courses/15-093…

MIT's Queues: Theory and Applications by Prof. David Gamarnik and Premal Shah Readings: ocw.mit.edu/courses/15-072…