Blog

Blog

Machine Learning 08 – Generalized Linear Models

We used probability theory – and an assumption that our variables were distributed on some specific distribution like the Bernoulli or the Gaussian distribution – to construct our „hypothesis“ function and our learning algorithms. In this Blog entry we will see that both the Bernoulli and the Gaussian distribution, even others like Poisson and Multinomial Machine Learning 08 – Generalized Linear Models weiterlesen

Machine Learning 05 – Probabilistic Interpretation of Least Squares Method

Machine Learning 05 – Probabilistic Interpretation of Least Squares Method

In this post, we will discuss and show, why the least squares method is actually the way it is. To do this, we have to use Probability theory and some probabilistic assumption on our various variables. Let’s say, that for every data-set in our training data the following rule applies: . While is an error value, which Machine Learning 05 – Probabilistic Interpretation of Least Squares Method weiterlesen

Machine Learning 01 – Introduction

Machine Learning 01 – Introduction

Machine Learning is a subset of Artificial Intelligence. Computer programs should predict an outcome without beeing explicitly programmed. Supervised Learning We talk about Supervised Learning whenever the machine gets training data to learn from. The machine is „supervised“ by the data. Types of Supervised Learning are Regression and Classification. Regression: A function is determined, which best Machine Learning 01 – Introduction weiterlesen