The Optimal Policy

Machine Learning & Stuff.

How do we call a C function from Python?

Need to speed up things by calling a C function from your Python script? Check this.

This will be a really short blogpost which mainly serves as a personal reminder on how to call a C function from Python… the title is pretty self-explanatory. I will show how to call some really s...

Posted by Andrea Bonvini on March 3, 2021

GANs - The Original Formulation

First blog-post dedicated to Generative Adversarial Networks

Let’s start with a question: What is Generative Modeling? Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regular...

Posted by Andrea Bonvini on December 15, 2020

The Hazard Function

How do we derive the Hazard Function in the context of Survival Anaysis?

In Survival Analysis we are interested in understanding the risk of an event happening at a particular point in time, where time is a continuous variable. For example, let’s consider the event fir...

Posted by Andrea Bonvini on December 14, 2020

LOTUS Theorem in GANs

How do we apply LOTUS Theorem to GANs' original value-function.

In this brief we want to prove a passage of the original GANs Paper by Ian Goodfellow (check this one if you want a full overview of the paper). Specifically, we want to prove that the following eq...

Posted by Andrea Bonvini on December 13, 2020

Support Vector Machines

The math behind SVMs

Introduction & Brief History In this blog-post we are gonna talk about one of the most powerful and fascinating techniques in Machine Learning: about the Support Vector Machine. In the fiel...

Posted by Andrea Bonvini on December 11, 2020

Kernel Methods

What's the use of Kernels in Machine Learning?

Prerequisites: Linear Algebra Ridge Regression (only if you want to understand the example provided) Hi everyone! In this brief blogpost we’ll talk about the use of Kernels in Machine Learn...

Posted by Andrea Bonvini on December 4, 2020

What's the VC dimension?

A quick overview.

Let’s start with some definitions: A hypothesis is a function that maps an input from the entire input space to a result: \(h:\mathcal{X}\to\{-1,+1\}\) The number of hypotheses $\vert\mathcal{H}\v...

Posted by Andrea Bonvini on December 2, 2020

Logistic Regression

A quick overview.

Although the name might confuse, please note that this is a classification algorithm. Considering a problem of two-class classification, in logistic regression the posterior probability of class $C_...

Posted by Andrea Bonvini on November 30, 2020

Linear Regression

Let's start with the basics.

Hi everyone, today’s topic is Linear Regression, one of the most basic, nevertheless powerful, techniques in machine learning. In order to fully understand this brief lecture, you should know the b...

Posted by Andrea Bonvini on November 28, 2020

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Machine Learning Classification Generative Adversarial Networks Generative Modeling Kernel Methods
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