Learning never stops! #machinelearningcode #machinelearningtheory #parametericlearning

Some great notes on convex scoring rule function in a multi-agent for learning #gametheory #machinelearningtheory #submodularity

60 students are attending our 1st in-person #machinelearningtheory summer school this week. Organizer @BorisHanin, assistant prof of operations research & financial engineering, aims to promote "a common language" to connect the next gen. of researchers in the field.

Machine Learning in Theory vs. Real-World.🙄 #machinelearningtheory #realworldapplication

RT Kernel Machine From Scratch https://t.co/1sE532h3Zw #kerneltrick #datascience #machinelearning #machinelearningtheory

RT When Is Bayesian Machine Learning Actually Useful? https://t.co/6PMEwqYhCc #machinelearningtheory #bayesianmachinelearning

🚀 Day 86 of Learning #DataScience ✅ Hashing in DSA ✅ Build a Packaged Food Ingredients Summarizer - Toxic or Healthy #100DaysOfCode #DataScience #Python #DSA #TechJourney #pythonlearning #CodingJourney #CodingChallenge #machinelearningtheory #LearningJourney #DataScience

Check out the full paper with @montasser_omar & John Lafferty! * Paper: https://t.co/STTKX2sDNs * Blog: https://t.co/tgkZeKZ2LY And come by our poster at NeurIPS in San Diego: https://t.co/EHZt4twuko #NeurIPS2025 #MachineLearningTheory #LLM #ChainOfThought [10/10]

👉 I am Learning Z-test and T-test feels simple and easy or I am wrong 😂. It's just the Start of EDA. 🗿Great teacher Krish Naik Sir. #100DaysOfCode #DataScience #Python #DSA #TechJourney #pythonlearning #CodingJourney #CodingChallenge #machinelearningtheory #LearningJourney

RT Theory of learning — a Bayesian perspective of generalization https://t.co/XmsjvRe2si #machinelearningtheory #pacbayes #bayesianmachinelearning

- Please vote - MachineLearningTheory https://t.co/OuB7ITAfpO

Balancing Model Fit and Generalization: The Bias Variance Tradeoff https://t.co/EYkc1bnpu3 #biasvariancetradeoff #machinelearning #modelselection #regularization #hyperparametertuning #crossvalidation #stem #machinelearningtheory #datascience

Bias-variance tradeoff is a fundamental concept in ML. Bias refers to error from overly simplistic models, while variance refers to error from overly complex models. Balancing these errors helps create models that generalize well to new data. #MachineLearningTheory

Machine Learning the Future Class - MachineLearningTheory https://t.co/jFbmFlPSD4

- ICML is changing its constitution - MachineLearningTheory https://t.co/ZeyL4iGZ1q

- Vowpal Wabbit version 8.3 and tutorial - MachineLearningTheory https://t.co/swpl1Hz1FQ

The Real World Interactive Learning Tutorial - MachineLearningTheory https://t.co/9JCqQbOhN7

- ICML Board and Reviewer profiles - MachineLearningTheory https://t.co/HFaRy4LvUz

- ICML is changing its constitution - MachineLearningTheory https://t.co/ZeyL4iGZ1q