Traditional learning systems have responded quickly to the COVID pandemic and moved to online or distance learning. Online learning requires a personalization method because the interaction between learners and instructors is minimal, and learners have a specific learning method that works best for them. One of the personalization methods is detecting the learners' learning style. To detect learning styles, several works have been proposed using classification techniques. However, the current detection models become ineffective when learners have no dominant style or a mix of learning styles. Thus, the objective of this study is twofold. Firstly, constructing a prediction model based on regression analysis provides a probabilistic approach for inferring the preferred learning style. Secondly, comparing regression models and classification models for detecting learning style. To ground our conceptual model, a set of machine learning algorithms have been implemented based on a dataset collected from a sample of 72 students using visual, auditory, reading/writing, and kinesthetic (VARK's) inventory questionnaire. Results show that regression techniques are more accurate and representative for real-world scenarios than classification algorithms, where students might have multiple learning styles but with different probabilities. We believe that this research will help educational institutes to engage learning styles in the teaching process.

We investigate the extent to which verb alternation classes, as described by Levin (1993), are encoded in the embeddings of Large Pre-trained Language Models (PLMs) such as BERT, RoBERTa, ELECTRA, and DeBERTa using selectively constructed diagnostic classifiers for word and sentence-level prediction tasks. We follow and expand upon the experiments of Kann et al. (2019), which aim to probe whether static embeddings encode frame-selectional properties of verbs. At both the word and sentence level, we find that contextual embeddings from PLMs not only outperform non-contextual embeddings, but achieve astonishingly high accuracies on tasks across most alternation classes. Additionally, we find evidence that the middle-to-upper layers of PLMs achieve better performance on average than the lower layers across all probing tasks.

Learning conditional densities and identifying factors that influence the entire distribution are vital tasks in data-driven applications. Conventional approaches work mostly with summary statistics, and are hence inadequate for a comprehensive investigation. Recently, there have been developments on functional regression methods to model density curves as functional outcomes. A major challenge for developing such models lies in the inherent constraint of non-negativity and unit integral for the functional space of density outcomes. To overcome this fundamental issue, we propose Wasserstein Distributional Learning (WDL), a flexible density-on-scalar regression modeling framework that starts with the Wasserstein distance $W_2$ as a proper metric for the space of density outcomes. We then introduce a heterogeneous and flexible class of Semi-parametric Conditional Gaussian Mixture Models (SCGMM) as the model class $\mathfrak{F} \otimes \mathcal{T}$. The resulting metric space $(\mathfrak{F} \otimes \mathcal{T}, W_2)$ satisfies the required constraints and offers a dense and closed functional subspace. For fitting the proposed model, we further develop an efficient algorithm based on Majorization-Minimization optimization with boosted trees. Compared with methods in the previous literature, WDL better characterizes and uncovers the nonlinear dependence of the conditional densities, and their derived summary statistics. We demonstrate the effectiveness of the WDL framework through simulations and real-world applications.

Machine learning is the technique that enables a machine to learn from data, improve performance from experiences and predict things without being explicitly programmed. Machine learning is a specialized technology that comes under Artificial Intelligence and it also includes self-driving cars, image recognition, and speech recognition. It is different from traditional programming, In machine learning when we pass data and output as the input it creates the model and gives the desired algorithm. Supervised Learning is a machine learning technique that can only process the labeled data. The model can be created by labeled data to know the datasets and to know about each data while training the model.

For starters, deployment is the process of integrating a trained machine learning model into a production environment, usually intended to serve an end-user. Deployment is typically the last stage in the development lifecycle of a machine learning product. The "Model Deployment" stage above consists of a series of steps which are shown in the image below: For the purpose of this tutorial, I will use Flask to build the web application. In this section, let's train the machine learning model we intend to deploy. For simplicity and to not divert from the primary objective of this post, I will deploy a linear regression model.

Mixture models are widely used to fit complex and multimodal datasets. In this paper we study mixtures with high dimensional sparse latent parameter vectors and consider the problem of support recovery of those vectors. While parameter learning in mixture models is well-studied, the sparsity constraint remains relatively unexplored. Sparsity of parameter vectors is a natural constraint in variety of settings, and support recovery is a major step towards parameter estimation. We provide efficient algorithms for support recovery that have a logarithmic sample complexity dependence on the dimensionality of the latent space. Our algorithms are quite general, namely they are applicable to 1) mixtures of many different canonical distributions including Uniform, Poisson, Laplace, Gaussians, etc. 2) Mixtures of linear regressions and linear classifiers with Gaussian covariates under different assumptions on the unknown parameters. In most of these settings, our results are the first guarantees on the problem while in the rest, our results provide improvements on existing works.

Every single data transformation we do in Artificial intelligence seeks to convert input-data to the most representative format required for the task we aim to solve… This conversion is done through functions. A machine-learning model transforms its input data into meaningful outputs. A process that is "learned" from exposure to known examples of inputs and outputs. Thus, the ML-model "learns a function" that maps its input data to the expected output. We have a table of a few data points, some belong to a "white" class and others to a "black" class.

A ubiquitous problem in machine learning, statistics, and signal processing is to accurately estimate an unknown sparse vector from a few noisy linear measurements. This estimation problem, which we refer to as sparse coding, is at the heart of the field of compressed sensing, revealing that under sparsity assumptions it is possible to successfully recover a signal that sampled significantly below the Nyquist rate [1, 2]. This, in turn, led to a dramatic increase in magnetic resonance imaging (MRI) scanning session speed [3]. Another exciting application that also builds on the sparsity assumption is unsupervised representation learning, i.e., given high-dimensional input data, such as an image, finding a low-dimensional representation that captures the intrinsic underlying structure in the input [4, 5, 6]. These representations are often used in image restoration tasks to effectively remove noise (denoising) [7, 8], fill-in missing pixels (inpainting) [9, 10, 11], and to achieve high quality digital zoom (super-resolution) [10, 12, 13, 14]. Sparsity also plays a key role in linear regression when given a large pool of features, to form a predictive rule that estimates an unknown response using a smaller, interpretable subset of features that manifests the strongest effects [15, 16, 17, 18]. To formalize the sparse coding problem, which is central for tackling the aforementioned applications, we consider the following linear model: b = Ax + v, where A is a matrix of size M N, the vector x is of length N, and v is a noise vector of length M. In this paper, we focus on a challenging setting in which M N, where a crucial assumption we make is that the vector x is k-sparse, i.e., it contains only k non-zero elements with k N [2, 1, 19].

Privacy-preserving data analysis has become prevailing in recent years. In this paper, we propose a distributed group differentially private majority vote mechanism for the sign selection problem in a distributed setup. To achieve this, we apply the iterative peeling to the stability function and use the exponential mechanism to recover the signs. As applications, we study the private sign selection for mean estimation and linear regression problems in distributed systems. Our method recovers the support and signs with the optimal signal-to-noise ratio as in the non-private scenario, which is better than contemporary works of private variable selections. Moreover, the sign selection consistency is justified with theoretical guarantees. Simulation studies are conducted to demonstrate the effectiveness of our proposed method.

Subsampling or subdata selection is a useful approach in large-scale statistical learning. Most existing studies focus on model-based subsampling methods which significantly depend on the model assumption. In this paper, we consider the model-free subsampling strategy for generating subdata from the original full data. In order to measure the goodness of representation of a subdata with respect to the original data, we propose a criterion, generalized empirical F-discrepancy (GEFD), and study its theoretical properties in connection with the classical generalized L2-discrepancy in the theory of uniform designs. These properties allow us to develop a kind of low-GEFD data-driven subsampling method based on the existing uniform designs. By simulation examples and a real case study, we show that the proposed subsampling method is superior to the random sampling method. Moreover, our method keeps robust under diverse model specifications while other popular subsampling methods are under-performing. In practice, such a model-free property is more appealing than the model-based subsampling methods, where the latter may have poor performance when the model is misspecified, as demonstrated in our simulation studies.