# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 3 Artificial Intelligence toward Learning Techniques
### Machine Learning, Neural Networks and Deep Learning  
All the conventional approaches discussed in the previous sections can be implemented by learning algorithms that let the corresponding network learn by a given dataset and, after performing a test with a sample dataset, can be used with a known predictive power. In this section, we get into details of the different machine learning techniques as a subset of AI methods (Figure 14). Figure 13: Pathway analysis performed on serum spectra recorded by \({}^{1}\)H NMR allowing the identification of main metabolic pathways associated with non-small cell lung cancer. The larger the circle, the higher the impact. The color, from red to yellow, identifies the corresponding significance. Figure reprinted from [23] under the terms of the Creative Commons Attribution 4.0 International License. In addition, neural networks and deep learning approaches are characterized in terms of the number of node layers, also named depth. Briefly, a node is the locus in which the algorithm performs the calculation and would correspond to the action that a neuron exerts in the human brain when it is subject to a stimulus. As shown in Figure 15b, a node takes different inputs, each having its own weight, that can be amplified or reduced by the activation function, thus giving a corresponding significance to the received inputs with respect to the specific task that the used algorithm is learning. So, a neural network consisting of two or more hidden layers can be classified as a deep learning technique and is usually described by the diagram shown in Figure 15a, together with a scheme of how one node might look (Figure 15b). Deep learning techniques, being able to handle large datasets, thus allowing a high-level description, are already used to provide the optimal route to solve a lot of issues in the field of image recognition, speech recognition, and natural language processing. Furthermore, DL techniques can be divided into three main categories (see Figure 16) that are deepened in Ref. [104]:  
* Supervised learning (discriminative) includes multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and gated recurrent unit (GRU);
* Unsupervised learning (generative) includes generative adversarial network (GAN), autoencoder (AE), sparse autoencoder (SAE), denoising autoencoder (DAE), contrac  
Figure 14: Venn diagram illustrating that deep learning is the core of machine learning, which in turn is a technique within AI methods. Figure 15: (**a**) Example scheme of a deep neural networks, reprinted from Ref. [103] under the terms of the CC-BY license; (**b**) operating principle of a single node. tive autoencoder (CAE), variational autoencoder (VAE), self-organizing map (SOM), restricted Boltzmann machine (RBM) and deep belief network (DBN);
* Hybrid learning (both discriminative and generative) includes models composed by both supervised and unsupervised algorithms other than deep transfer learning (DTL) and deep reinforcement learning (DRL). Supervised learning can furnish a discriminative function in classification applications by identifying the different features of those classes that can be extracted by the data. Among them, multi-layer perceptron (MLP) is a feedforward ANN that involves (i) an input layer collecting input signals, (ii) an output layer that provides an outcome in consideration of the processed input and (iii) some hidden layers separating the input and output layers that correspond to the network computational engine. On the contrary, unsupervised learning is employed to recognize eventual correlations by analyzing the signals pattern and to assess the statistical distributions of the achieved results both on original data and on their corresponding classes. This kind of generative approach can be also used as an initial step (pre-processing) before applying supervised learning methods. Most common unsupervised techniques, reported in Figure 16, are listed and briefly described in the next.