As we will briefly see, Kernels will have an important role in many machine learning applications. In this note we will get to know what are Kernels and why are they useful. Intuitively they measure the similarity between two input points. So if they are close the kernel should…
Gaussian processes can be viewed through a Bayesian lens of the function space: rather than sampling over individual data points, we are now sampling over entire functions. They extend the idea of bayesian linear regression by introducing an infinite number of feature functions…
There is a big difference between the empirical score and the expected score; in the beginning, we had said something about this in Introduction to Advanced Machine Learning . We will develop more methods to better comprehend this fundamental principles. How can we estimate the…
This note used the definitions present in Provably Approximately Correct Learning . So, go there when you encounter a word you don't know. Or search online Rademacher Complexity # Given an hypothesis set H , we define a family of loss functions as: G = { g : ( x , y ) → L ( h (…
This is also known as Lagrange Optimization or undetermined multipliers . Some of these notes are based on Appendix E of (Bishop 2006) , others were found when studying bits of rational mechanics. Also (Boyd & Vandenberghe 2004) chapter 5 should be a good resource on this…
Anomaly detection is a problem in machine learning that is of a big interest in industry. For example a bank needs to identify problems in transactions, doctors need it to see illness, or suspicious behaviors for law (no Orwell here). The main difference between this and…
Softmax is one of the most important functions for neural networks. It also has some interesting properties that we list here. This function is part of The Exponential Family , one can also see that the sigmoid function is a particular case of this softmax, just two variables.…
Introduction to the course # Machine learning offers a new way of thinking about reality: rather than attempting to directly capture a fragment of reality, as many traditional sciences have done, we elevate to the meta-level and strive to create an automated method for capturing…
Queste note sono molto di base. Per cose leggermente più avanzate bisogna guardare Bayesian Linear Regression , Linear Regression methods . Introduzione alla logistic regression # Giustificazione del metodo # Questo è uno dei modelli classici, creati da Minsky qualche decennio…
(Schulman et al. 2017) è uno degli articoli principali che praticamente hanno dato via al campo. Anche questo è buono per Policy gradients: https://lilianweng.github.io/posts/2018-04-08-policy-gradient/ See RL Function Approximation , this document is deprecated. References #…