## 2 Brief Overview of Machine Learning  
Within ML, one can largely divide the field through families of methods, such as \"symbolist,\" \"connectionist,\" \"Bayesian,\" \"evolutionaries,\" and \"analogizers,\" as summarized in Table 1[11; 12]. Notably, while \"analogizers\" semantically appear to be related to \"analogical inference,\" this is only tangential, as the methods in this space refer to spatial similarity of classes based on distances between groupings in a data space [11]. Alternatively, ML algorithms can also be described in terms of different paradigms as it pertains to training, such as \"supervised,\" \"unsupervised,\" or \"semi-supervised\" [13]. Thus, to understand how these are related but differ from the topics of interest--transfer learning and analogical inference--there is a need to formally establish the major differences between common types of machine learning, as shown in Figure 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Family of Learning** & **Description** \\ \hline Symbolist (AKA inductive) & An inductive-reasoning-based method which is based on representing concepts through “symbols” which can be mathematically manipulated \\ \hline Connectionism & A bottom-up approach inspired by the brain’s ability to function based on firing neurons (“activation”) \\ \hline Bayesian & A technique based using on estimated event probability to predict outcomes, popular in causal situations \\ \hline Evolutionary & A biological-inspired approach based on the ability to evolve and mutate in hopes of achieving better performance \\ \hline Analogizers & A method based on identifying patterns and similarities between two domains and learning through various types of reasoning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of [PERSON]’s ML algorithm family definitions (see [11] for original). All ML applications can be split into three categories: supervised, unsupervised, or semi-supervised learning. Supervised learning (large dark-grey section on the leftmost side of Figure 1) involves the prediction of a classification label or value (in the case of regression) where the data (used for training and testing) are all labeled/known during the evaluation [13; 14]. On the other hand, unsupervised learning (light-grey section on the rightmost side of Figure 1) only utilizes unlabeled data, typically for clustering or association problems [13; 15]. Sometimes, both supervised and unsupervised learning methods can be utilized in a hybrid approach between the two where some of the data are labeled and some are not labeled; this is called semi-supervised learning (black section of Figure 1) [13; 14; 16]. In addition to the three broadest types of learning, other specialized learning algorithms have been developed in the past decade. Self-supervised learning is a subset of unsupervised or semi-supervised methods where the algorithm creates \"pseudo-labels\" to mimic and be solved as if it was a supervised problem [17]. Within self-supervised learning algorithms, contrastive learning is a popular approach in which pseudo-labels are created by comparing the data samples to one another to capture the similarity between samples. The learned feature representations then impact the results of downstream tasks, such as classification/clustering of the data [17; 18]. Most recently, contrastive learning has included significant efforts involving Generative Adversarial Networks (GANs). In the computer vision realm, GAN methods consider the curation and expansion of datasets when limited data are available for training [19]. Depending on how much original data are labeled, if any, self-supervised and contrastive learning algorithms fall either in the greater domains of semi-supervised or unsupervised learning.