I’m currently in the process of writing the background chapter on Machine Learning for my dissertation. In the context of doing that, I took a closer look at a widely used feature extraction technique called “Principle Component Analysis” (PCA). It can be described either on the intuitive level (“it finds orthogonal directions of greatest variance in the data”) or on the mathematical level (“it computes an eigenvalue decomposition of the data matrix”). What I found most difficult to understand was how these two descriptions are related to each other. This is essentially what I want to share in today’s blog post. Continue reading “What is a “Principle Component Analysis”?”
In the past, we have already talked about some machine learning models, including LTNs and β-VAE. Today, I would like to introduce the basic idea of linear support vector machines (SVMs) and how they can be useful for analyzing a conceptual space. Continue reading “What is a “Support Vector Machine”?”
I’ve already talked about InfoGAN  a couple of times (here, here, and here). InfoGAN is a specific neural network architecture that claims to extract interpretable and semantically meaningful dimensions from unlabeled data sets – exactly what we need in order to automatically extract a conceptual space from data.
InfoGAN is however not the only architecture that makes this claim. Today, I will talk about the β-variational autoencoder (β-VAE)  which uses a different approach for reaching the same goal.
I’ve already talked about how to potentially obtain the dimensions of a conceptual space with artificial neural networks in a previous blog post. That approach is based on machine learning techniques, but there’s also a more traditional way of extracting a conceptual space: Conducting a psychological experiment and using a type of algorithm called “multidimensional scaling”. Today, I would like to give a quick overview of this approach.
Over the past few weeks, I have been pretty busy fulfilling my teaching duties. As I haven’t done much researching, I won’t talk about research today, but about “Constructive Alignment”, which is an approach for planning lectures, seminars and other courses.
The constructive alignment process consists of three steps:
- Defining the learning targets
- Planning the examination
- Planning the course
But wait a second, why does planning the course appear as the last step in this process?