This chapter introduces a set of distances and scatter matrices of various kinds used to measure the difference or similarity between two sample points, one sample and one class/cluster, and two classes/clusters, and the within, between, and total scatteredness of classes/clusters, for the purpose of further measuring the separability of the classes/clusters in a subspace composed of features that are either selected or extracted from the original high dimensional feature space .

This chapter intruduces an important idea of kernel mapping, which can map the feature space to a much higher dimensional space where the class separability could be improved significantly for better classification results. Based on the assumption that all data samples only appear in the form of inner product in the algorithm, kernel mapping is actually carried out implicitly, in the sense that the mapping function never needs to be explicitly specified. The chapter then introduces the method of kernel PCA, as a variant of PCA, together with another variant probabilistic PCA. The chapter further considers the method of factor analysis based on two important concepts of latent variables and expectation maximization (EM), both playing some important roles in other learning algorithms to be discussed in future chapters. Finally the chapter moves on to discuss two additional methods, multidimensional scaling (MDS) and t-distributed stochastic neighbor embedding (t-SNE), for the same general purpose of dimensionality reduction.

This chapter discusses both supervised and unsupervised algorithms all to be carried out in a tree-like hierarchy, in which a classification or clustering problem is solved in a divide-and-conquer manner while traversing a binary tree. For supervised classification, the tree classifier is first constructed in the training phase, and then in the test phase, a set of classes are subdivided into two subsets at each node of the tree based on a subset of features specifically selected to best separate the two subsets. This operation is carried out along a path in the tree from the root node down to one of the leaf nodes representing one of the classes. For unsupervised clustering, the tree structure is constructed in either a top-down or ottom-up fashion. In the former case, the given dataset represented by root node is recursively split into two subsets represented by the two child nodes; while in the latter case, all samples each represented by one of the leaf nodes are merged sequentially until they form a single group at the root node. In either case, the splitting or merging is carried out based on certain distance previously considered. Such splitting or merging process can be truncated somewhere between the root and leaf nodes to obtain a set of clusters.

This chapter discusses nonlinear regression method based on gradient descent and its variations for obtaining the optimal parameters of any given nonlinear regression function.

This chapter is dedicated to the sole topic of support vector machine (SVM), a typical discriminative algorithm mostly for binary classification. The goal of the algorithm is to find a optimal hyperplane that separate the two classes (assumed to be linearly separable) in the feature space in such a way that the two classes are best separated, in the sense that the distances (called margin) between the plane and the samples closest to it (called support vectors) on either side of the plane are maximized. This is a constrained optimization problem which could be solved directly, but it is actually first converted to its dual problem and then solved by quadratis programming. The reason for solving the dual problem is due to the fact that all data points appear in the form of inner product, so that kernel method can be used to carry out the classification in a higher dimensional space in which the two classes become linearly separable even if they are not so in the original space. The chapter further considers some variants of SVM, such as sequential minimal optimization and generalized multiclass SVM.

Thia chapter considers methods for both regression and classification based on Gaussian process, a stochastic process with Gaussian distribution, of which the mean vector and covariance matrix can be obtained based on the labeled samples in the training set. The resulting Gaussian process serves as a nonlinear regression function that fits the given dataset. This function can be treated as the probability for data samples' the class identity and used for classificationas as shown before. This Gaussian process approach also has some two advantages: first, the certainty (or confidence) of the regression or classification result can be quantitatively measured; second proper tradeoff between overfitting and underfitting can be made by adjusting a parameter for the covariance of the Gaussian process model.

Zooarchaeological research has transformed our knowledge about relationships among animals and people. We have a much better understanding of the diverse ways in which people respond to the challenges and opportunities of their environments; the variety of roles animals fill; the breadth of animals’ social meanings; the importance of cuisines in sustaining our biological and social lives; and the magnitude of our impact on the environment. and is increasingly informed by technical and theoretical advances as members of interdisciplinary teams. From this holistic perspective on the human condition, we gain a better understanding of our past, present, and future.

Zooarchaeology is the study of animal remains excavated from archaeological sites. The goal of zooarchaeology is to understand human relationships with the environment through their interactions with nonhuman animals. Zooarchaeology is widely interdisciplinary, global in scope, and practiced by a diverse, interconnected community of scholars with a wide range of experiences, theoretical interests, training, and methodologies.

This chapter is solely dedicated to reinforcement learning (RL), one of the three main learning paradigms covered in the book (together with regression and classification). The goal of RL is for an agent to learn from and respond to its environment modeled as a Markov decision process (MDP), by following a set of policies to take the best action at each state of the MDP, in order to receive the maximum total accumulated reward. The utmost goal is to come up with the optimal policy in terms of the best action to take at each state. Different from all optimization problems previously considered for maximizing (or minimizing) certain objective functions, RL achieves its goal by the general method of dynamic programming (while linear and quadratic programmings are for constrained optimization), which solves a complex problem by breaking it up and solving a set of subproblems recursively. Specifically, the main method for RL is the Q-learning algorithm which finds the optimal policy that takes the best action selected based on the expected values of the total reward at all states and all actions at each state. Toward to end of the chapter, various more advanced versions of RL are briefly discussed based on some previously learned methods such as neural networks and deep learning.

The practice of zooarchaeology requires familiarity with the types of animals represented in archaeological assemblages, particularly with hard tissues most likely to be present in the archaeological record. This knowledge must be grounded in a basic understanding of taxonomy (both folk and systematic), evolution, anatomy, and morphology. Modern zooarchaeology also requires familiarity with highly technical analyses, such as archaeogenetics, stable isotopes, and trace elements.

The goal of this chapter is to prepare for the future discussion of various artificial neural network (ANN) learning algorithms by introducing some basic concepts in neural networks and some biologically inspired examples the Habbian and Hopfield networks to illustrate how an ANN based on some simple learning rule can achieve meaningful results, although they are not actually widely used in machine learning practice. Specifically, the behavior of the Hebbian learning network mimics the associative nature of brain, as a simple model of associative memory, and the Hopfield network further shows how a pattern can be stored and then recalled based on a noisy and imcomplete copy of itself, a function that is commenly demonstratedof the brain.