While Active Learning looks for the most informative points to recover a true underlying function, Bayesian Optimization is just interested to find the maximum of that function. In Bayesian Optimization, we ask for the best way to find sequentially a set of points x 1 , … , x…
Andiamo a parlare di processi Markoviani. Dobbiamo avere bene a mente il contenuto di Markov Chains prima di approcciare questo capitolo. Markov property # Uno stato si può dire di godere della proprietà di Markov se, intuitivamente parlando, possiede già tutte le informazioni…
These algorithms are good for scaling state spaces, but not actions spaces. The Gradient Idea # Recall Temporal difference learning and Q-Learning, two model free policy evaluation techniques explored in Tabular Reinforcement Learning . A simple parametrization # The idea here…
Merkle Trees: A Fundamental Structure in Cryptography Merkle trees, introduced by Ralph Merkle in 1979, are a pivotal data structure in cryptographic systems. These binary hash trees enable efficient and secure verification of data integrity within distributed systems. Their…
Introduzione sull'encoding # Ossia trattiamo metodi per codificare caratteri dei linguaggi umani, come ASCII, UCS e UTF. Digitalizzare significa encodarlo in un sistema che possa essere memorizzato su un dispositivo di memorizzazione elettronico. Ovviamente non possiamo…
We have first cited the graph data model in the Introduction to Big Data note. Until now, we have explored many aspects of relational data bases, but now we are changing the data model completely. The main reason driving this discussion are the limitations of classical…
We have a prior p ( model ) , we have a posterior p ( model ∣ data ) , a likelihood p ( data ∣ model ) and p ( data ) is called the evidence . Classical Linear regression # Let's start with a classical regression. In this setting we need to estimate a model that is generated…
DI Law of Large Numbers e Central limit theorem ne parliamo in Central Limit Theorem and Law of Large Numbers . Usually these methods are useful when you need to calculate following something similar to Bayes rule, but don't know how to calculate the denominator, often…
With variational inference we want to find a good approximation of the posterior distribution from which it is easy to sample. The objective is to approximate the posterior with a simpler one, because sometimes the prior or likelihood are difficult to compute. p ( θ ∣ x 1 : n …
There is huge literature on planning. We will attack this problem from the view of probabilistic artificial intelligence. In this case we focus on continuous, fully observed with non-linear transitions, an environment often used for robotics. It's called Model Predictive Control…