Artificial Intelligence (AI) and Machine Learning (ML) are the current hot assets of technology and disrupting almost all possible domains and sectors, including education. However, not many of us are aware that much of AI's theoretical and technological underpinning dates back to nearly seven decades ago, thanks to scientists such as Alan Turing, Marvin Minsky, and John McCarthy. In simple terms, AI is a sequence of technologies that power machines to function with higher intelligence levels, almost emulating human capabilities. As matter of fact, Artificial Intelligence has had a profound impact on the nature of services within the education sector. With respect to developing economies, the population's education and literacy levels play a significant role in the overall transition to an advanced economy.

We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototype-based Learning Vector Quantization (LVQ) for classification and shallow, layered neural networks for regression tasks. We investigate so-called student teacher scenarios in which the systems are trained from a stream of high-dimensional, labeled data. Properties of the target task are considered to be non-stationary due to drift processes while the training is performed. Different types of concept drift are studied, which affect the density of example inputs only, the target rule itself, or both. By applying methods from statistical physics, we develop a modelling framework for the mathematical analysis of the training dynamics in non-stationary environments. Our results show that standard LVQ algorithms are already suitable for the training in non-stationary environments to a certain extent. However, the application of weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes. Furthermore, we investigate gradient-based training of layered neural networks with sigmoidal activation functions and compare with the use of rectified linear units (ReLU). Our findings show that the sensitivity to concept drift and the effectiveness of weight decay differs significantly between the two types of activation function.

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.

In present-day history, 2020 was an extraordinary year. Over the most recent hundred years, humanity has not gone through a worldwide pandemic like COVID-19. All countries, businesses, and practically all people on our planet have been influenced by it. Fortunately, we have antibodies on our doorsteps and can at long last, with a ton of fervor and expectation, welcome the New Year 2021 with the best asp .net The advanced change has been incredibly quickened by COVID-19, and the cycle will be more noteworthy in 2021.

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This scholarship has been established to provide financial assistance to a PhD student to undertake research funded by an ARC Grant headed by Buhui Qiu within the University of Sydney Business School. The project will develop an innovative machine-learning-based approach for measuring, monitoring and evaluating bank lending activities and risk disclosures to take advantage of the big data available. It will use multidimensional data to produce more relevant metrics for assessing bank risks and risk disclosure quality and apply them in regulatory policy evaluation. The project findings will significantly advance the knowledge on mitigating banking misconduct. They will also equip regulatory authorities with an efficient monitoring tool and an early-warning device to promote better lending and risk disclosure practices, and foster a more transparent and stable financial system to support financial intermediation in Australia and worldwide.

I found that many university courses, books or online training either taught me how to build software or how to train machine learning (ML) models, but few blended both worlds. Having worked on various AI projects in the IBM Garage, I want to share how my experience in using test-driven development (TDD) helped me build better AI-powered applications. This blog post uses an AI-powered web app as an example on how to apply TDD in AI projects. In the web app people can upload images taken inside and outside of houses. Then the website will display whether the photo was taken outside or, if taken inside, in which room.

Model optimization is one of the toughest challenges in the implementation of machine learning solutions. Entire branches of machine learning and deep learning theory have been dedicated to the optimization of models. Hyperparameter optimization in machine learning intends to find the hyperparameters of a given machine learning algorithm that deliver the best performance as measured on a validation set. Hyperparameters, in contrast to model parameters, are set by the machine learning engineer before training. The number of trees in a random forest is a hyperparameter while the weights in a neural network are model parameters learned during training.

As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post hoc techniques which provide recourse to affected individuals. These techniques generate recourses under the assumption that the underlying predictive model does not change. However, in practice, models are often regularly updated for a variety of reasons (e.g., dataset shifts), thereby rendering previously prescribed recourses ineffective. To address this problem, we propose a novel framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial training for finding recourses that are robust to model shifts. To the best of our knowledge, this work proposes the first solution to this critical problem. We also carry out detailed theoretical analysis which underscores the importance of constructing recourses that are robust to model shifts: 1) we derive a lower bound on the probability of invalidation of recourses generated by existing approaches which are not robust to model shifts. 2) we prove that the additional cost incurred due to the robust recourses output by our framework is bounded. Experimental evaluation on multiple synthetic and real-world datasets demonstrates the efficacy of the proposed framework and supports our theoretical findings.

Due to heightened concerns regarding the outbreak of COVID-19, we are adding more instructor-led online training courses as an alternative to classroom courses. This course is also offered in an online, self-paced format. This two-day course focuses on data analytics and machine learning techniques in MATLAB using functionality within Statistics and Machine Learning Toolbox and Deep Learning Toolbox . The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results.