Foundations of ML:

TRUE or FALSE: If two unbiased estimators of a parameter are given, then the average of these two estimators is always unbiased and must also have variance no larger than the variance of either estimator.

No dev team? No problem. Caffeine turns your ideas into launch-ready apps — just by chatting. Start for free →

Kernel methods provide a flexible way to model complex, nonlinear relationships by implicitly mapping data into high-dimensional feature spaces. In statistics and probability, they enable smooth density estimation, hypothesis testing, and dependence measurement. In machine

Stop by the @BlackBoxAI Lab, where your coding agents are always on call. Send one remote task and let multiple agents handle all the heavy lifting.

TRUE or FALSE: If the maximum likelihood estimator in a regular statistical model satisfies an asymptotic normal distribution, then it must also be asymptotically unbiased.

Declutter your ride in seconds with a smart organizer that keeps essentials upright and easy to grab.

The paper reinterprets Maximum Entropy RL as a diffusion-based sampling problem, minimizing reverse KL between a diffusion policy and the optimal policy via an upper bound. Using the policy gradient theorem, it derives a new surrogate objective that embeds diffusion dynamics.

TRUE or FALSE: For a symmetric random walk on ℤ, the expected time to return to zero is finite.

Inverse reinforcement learning (IRL) aims to recover the reward function that explains an expert’s behavior rather than learning a policy directly. By observing actions, IRL infers what agents value. In probability and statistics, it uses Bayesian inference and likelihood models

Batch reinforcement learning trains agents using fixed, pre-collected datasets instead of online interaction. It emphasizes stability and safety because the data may not cover all situations. In probability and statistics, it uses uncertainty estimation and off-policy evaluation.

Plinbo: Roguelike Plinko!ִֶָ 𓂃 ࣪˖ ִֶָ🏐་༘࿐ Upgrade your balls right now. Play Free Demo on Steam!

A Best Linear Unbiased Estimator (BLUE) is a linear estimator that has no systematic bias and achieves the lowest possible variance among all unbiased linear estimators. Based on the Gauss–Markov theorem, the ordinary least squares estimator is BLUE when errors are uncorrelated

Gibbs sampling is a Markov chain Monte Carlo method that generates samples from complex joint distributions by iteratively sampling each variable from its conditional distribution. In probability and statistics, it enables Bayesian inference when direct sampling is hard. In ML,

Which field of study do you think has the highest ROI?

TRUE or FALSE: If two unbiased estimators of a parameter are given, then the average of these two estimators is always unbiased and must also have variance no larger than the variance of either estimator.

Find out how is SG leading Southeast Asia’s energy transition through increased renewable financing and project development, production as well as distribution.

TIME SERIE & QUANTUM ML: Quantum machine-learning algorithms have recently gained strong interest on photonic platforms. Reconfigurable integrated photonic circuits are especially promising because adaptive feedback enables the nonlinear behavior needed for neural-network–like

Good Morning!

Which field of study do you think has the highest ROI?

Gibbs sampling is a Markov chain Monte Carlo method that generates samples from complex joint distributions by iteratively sampling each variable from its conditional distribution. In probability and statistics, it enables Bayesian inference when direct sampling is hard. In ML,

Love Mafia? Exploding Kittens? Tired of the same old shuffle? Meet Kasai & Bakra: the party game where your friends betray you, and the BBQ is real

A Best Linear Unbiased Estimator (BLUE) is a linear estimator that has no systematic bias and achieves the lowest possible variance among all unbiased linear estimators. Based on the Gauss–Markov theorem, the ordinary least squares estimator is BLUE when errors are uncorrelated

TRUE or FALSE: If A ⊆ B, then P(A | B) ≥ P(A).

TRUE or FALSE: If X and Y are identically distributed, then they must have the same variance.