The idea of cross-validation is to split the data into N subsets, to put one subset aside, to estimate parameters of the model from the remaining N-1 subsets, and to use the retained subset to estimate the error of the model. Such a process is repeated N times - with each of the N subsets being used as the validation set . Then the values of the errors obtained in such N steps are combined to provide the final estimate of the model error. The cross-validation is used in various classification and prediction procedures, such as regression analysis, discriminant analysis, neural networks and classification and regression trees (CART) . The goal is to improve the quality of the decision that is made from the outcome of the study on the basis of statistical methods, and to ensure that maximum information is obtained from scarce experimental data.

The Monty Hall problem was first featured on the classic game show "Let's make a Deal". In the final segment of the show, contestants were presented with a choice of three different doors. Behind two of the doors would be a goat, and behind the third would be an extravagant prize such as a car. The contestant begins the game by picking one door. The host, Monty Hall, would then open one of the remaining doors.

The connection between machine learning and statistical physics has long been appreciated [1-9], but deeper relationships continue to be uncovered. For example, techniques used to pre-train neural networks [8] have more recently been interpreted in terms of the renor-malization group [10]. In the other direction there has been a sharp increase in applications of machine learning to chemistry, material science, and condensed matter physics [11-19], which are sources of highly-structured data and could be a good testing ground for machine learning techniques. A recent trend in both physics and machine learning is an appreciation for the power of tensor methods. In machine learning, tensor decompositions can be used to solve non-convex optimization tasks [20, 21] and make progress on many other important problems [22-24], while in physics, great strides have been made in manipulating large vectors arising in quantum mechanics by decomposing them as tensor networks [25-27]. The most successful types of tensor networks avoid the curse of dimensionality by incorporating only low-order tensors, yet accurately reproduce very high-order tensors through a particular geometry of tensor contractions [27]. Another context where very large vectors arise is in nonlinear kernel learning, where input vectors x are mapped into a higher dimensional space via a feature map Φ( x) before being classified by a decision function f ( x) W · Φ( x).

Text mining (deriving information from text) is a wide field which has gained popularity with the huge text data being generated. Automation of a number of applications like sentiment analysis, document classification, topic classification, text summarization, machine translation, etc has been done using machine learning models. Spam filtering is a beginner's example of document classification task which involves classifying an email as spam or non-spam (a.k.a. Spam box in your Gmail account is the best example of this. So lets get started in building a spam filter on a publicly available mail corpus.

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us). Within Machine Learning many tasks are - or can be reformulated as - classification tasks. In classification tasks we are trying to produce a model which can give the correlation between the input data and the class each input belongs to. This model is formed with the feature-values of the input-data. For example, the dataset contains datapoints belonging to the classes Apples, Pears and Oranges and based on the features of the datapoints (weight, color, size etc) we are trying to predict the class. We need some amount of training data to train the Classifier, i.e. form a correct model of the data.

Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist today require a symbolic representation of motions (e.g. in the form of motion primitives), which have to be defined a-priori or require complex segmentation algorithms. In contrast, recent advances in the field of neural networks and especially deep learning have demonstrated that sub-symbolic representations that can be learned end-to-end usually outperform more traditional approaches, for applications such as machine translation. In this paper we propose a generative model that learns a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (RNNs) and sequence-to-sequence learning. Our approach does not require any segmentation or manual feature engineering and learns a distributed representation, which is shared for all motions and descriptions. We evaluate our approach on 2,846 human whole-body motions and 6,187 natural language descriptions thereof from the KIT Motion-Language Dataset. Our results clearly demonstrate the effectiveness of the proposed model: We show that our model generates a wide variety of realistic motions only from descriptions thereof in form of a single sentence. Conversely, our model is also capable of generating correct and detailed natural language descriptions from human motions.

And why is it useful for BigData? 29 What is "continuous features binning"? What is a Standard Scaling? 38 Why are statistical distributions important? What is a Bias - Variance tradeoff? What is a training set, a validation set, a test set and a gold set in supervised and unsupervised learning? What is a cross-validation and what is an overfitting?

An important problem in Bayesian networks classifiers (BNC) is to discover relevant variables that can achieve optimal classification performance. We propose a method based on Bayesian inference for estimating and incorporating feature relevance in classification using BNCs. We empirically validate our method on an application to event recognition in natural images using object and scene information.

Several problems requiere the combination of temporal and spatial reasoning under uncertainty, such as wind prediction for electricity generation in wind farms. In this work we propose a probabilistic spatial-temporal model (PSTM) focused on prediction problems, based on two common properties of these scenarios: sparsity and multivariable mutual information. The proposed spatial-temporal model is essentially a Bayesian network that represents the dependencies between a target variable of interest and a subset of predictor variables in different times and spaces. We developed an algorithm for learning the structure of the model based on a stochastic search of the optimal subset of predictor variables. The proposed model has been applied for wind prediction at different locations in Mexico, using information from several locations at different times. The PSTM is evaluated in terms of predictive accuracy for different time horizons — 1 to 24 hours; and compared to a dynamic Bayesian network (DBN) developed for wind prediction. The performance of the PSTM is in general competitive, and in most cases superior to the DBN.

Dynamic Bayesian Networks (DBNs) bring efficient tools to model complex multivariate dynamical systems learned from collected data and/or expert knowledge. Notwithstanding, the underlying generative Markov model is supposed homogeneous; neither its topology nor its parameters evolve over time. Thus, learning a DBN to model a non-stationary process with this belief will lead to poor prediction capabilities. In order to account for nonstationary processes, we build on a framework to identify transitions between underlying models and a framework to learn them in real time, without making hypothesis about their evolution. We present the tool performances on simulated datasets. Since we aim to use this to model and predict incongruities within an Intrusion Detection System (IDS) in near real-time, great care is ascribed to the capability to correctly detect transition times. Our prior results display the precision of our algorithm in the choice of transitions and therefore the quality of identified networks. At last we suggest future work.