This paper presents a 3-step system that estimates the real-time energy expenditure of an individual in a non-intrusive way. First, using the user's smart-phone's sensors, we build a Decision Tree model to recognize his physical activity (\textit{running}, \textit{standing}, ...). Then, we use the detected physical activity, the time and the user's speed to infer his daily activity (\textit{watching TV}, \textit{going to the bathroom}, ...) through the use of a reinforcement learning environment, the Partially Observable Markov Decision Process framework. Once the daily activities are recognized, we translate this information into energy expenditure using the compendium of physical activities. By successfully detecting 8 physical activities at 90\%, we reached an overall accuracy of 80\% in recognizing 17 different daily activities. This result leads us to estimate the energy expenditure of the user with a mean error of 26\% of the expected estimation.

Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces. In this paper, we technically propose a new enriched prior based Dual-constrained Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net, for learning the hierarchical coupled representations. To ex-tract hidden deep features, DS2CF-Net is modeled as a deep-structure and geometrical structure-constrained neural network. Specifically, DS2CF-Net designs a deep coupled factorization architecture using multi-layers of linear transformations, which coupled updates the bases and new representations in each layer. To improve the discriminating ability of learned deep representations and deep coefficients, our network clearly considers enriching the supervised prior by the joint deep coefficients-regularized label prediction, and incorporates enriched prior information as additional label and structure constraints. The label constraint can enable the samples of the same label to have the same coordinate in the new feature space, while the structure constraint forces the coefficient matrices in each layer to be block-diagonal so that the enhanced prior using the self-expressive label propagation are more accurate. Our network also integrates the adaptive dual-graph learning to retain the local manifold structures of both the data manifold and feature manifold by minimizing the reconstruction errors in each layer. Extensive experiments on several real databases demonstrate that our DS2CF-Net can obtain state-of-the-art performance for representation learning and clustering.

Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex associations. Currently, there are many tools to facilitate implementing custom ML analyses (e.g. scikit-learn). Interest is also increasing in automated ML packages, which can make it easier for non-experts to apply ML and have the potential to improve model performance. ML permeates most subfields of biomedical research with varying levels of rigor and correct usage. Tremendous opportunities offered by ML are frequently offset by the challenge of assembling comprehensive analysis pipelines, and the ease of ML misuse. In this work we have laid out and assembled a complete, rigorous ML analysis pipeline focused on binary classification (i.e. case/control prediction), and applied this pipeline to both simulated and real world data. At a high level, this 'automated' but customizable pipeline includes a) exploratory analysis, b) data cleaning and transformation, c) feature selection, d) model training with 9 established ML algorithms, each with hyperparameter optimization, and e) thorough evaluation, including appropriate metrics, statistical analyses, and novel visualizations. This pipeline organizes the many subtle complexities of ML pipeline assembly to illustrate best practices to avoid bias and ensure reproducibility. Additionally, this pipeline is the first to compare established ML algorithms to 'ExSTraCS', a rule-based ML algorithm with the unique capability of interpretably modeling heterogeneous patterns of association. While designed to be widely applicable we apply this pipeline to an epidemiological investigation of established and newly identified risk factors for pancreatic cancer to evaluate how different sources of bias might be handled by ML algorithms.

A subreddit dedicated for learning machine learning. Feel free to share any educational resources of machine learning. Also, we are a beginner-friendly sub-reddit, so don't be afraid to ask questions! This can include questions that are non-technical, but still highly relevant to learning machine learning such as a systematic approach to a machine learning problem.

Let's see if we can forecast Timmy's math grade using a Random Decision Forest… From virtual teaching assistants named Jill Watson and Happy Numbers to essay grading software like Gradescope, artificial intelligence has started seeping into schools, colleges, and universities. Although it's interesting to learn about the benefits and detriments of this development, I'm more fascinated with the following question: how can we use artificial intelligence and machine learning to improve student success? My first stab at this broad question was seeing if we could predict student performance based on student/parent participation. From my experience, teachers often encourage students to participate in class discussions, activities, and projects. In addition, schools usually encourage parents to take part in their child's education through parent teacher conferences, surveys, and meetings.

Central University of Technology, South Africa, is introducing an Artificial Intelligence university programme powered by Microsoft. To firstly skill employees with the in-demand skill and secondly address the demand for the skill in the province and South Africa in general. The Artificial Intelligence university programme is developed by Microsoft and will be delivered by Microsoft Partner Gijima. The initiative is also in partnership with the Free State Provincial Government. It will comprise of a 12-month blended learning model of self-study, online learning, classroom instructor-led training and a flipped classroom.

Over the course of an hour, an unsolicited email skips your inbox and goes straight to spam, a car next to you auto-stops when a pedestrian runs in front of it, and an ad for the product you were thinking about yesterday pops up on your social media feed. What do these events all have in common? It's artificial intelligence that has guided all these decisions. And the force behind them all is machine-learning algorithms that use data to predict outcomes. Now, before we look at how machine learning aids data analysis, let's explore the fundamentals of each.

There are two different types of AI in wide use today. Recent developments have focused on data-driven machine learning, but in the last decades, most AI applications in education (AIEd) have been based on representational / knowledge-based AI. Data-driven AI uses a programming paradigm that is new to most computing professionals. It requires competences which are different from traditional programming and computational thinking. It opens up new ways to use computing and digital devices. But the development of state-of-the-art AI is now starting to exceed the computational capacity of the largest AI developers. The recent rapid developments in data-driven AI may not be sustainable. The impact of AI in education will depend on how learning and competence needs change, as AI will be widely used in the society and economy.

Artificial Intelligence (AI) has been much talked about over the last few years. Several interpretations of the potential of AI and its outcomes have been shared by technologists and futurologists. With the focus on the customer, the possibilities range from predicting trends to recommending actions to prescribing solutions. The potential for change due to AI applications is energised by several factors. The first is the concept of AI itself which is not a new phenomenon.

If you google "how to learn skill " you're probably going to find at least one online course, youtube tutorial, book, or article covering it well. Many of these resources will even be for free. When it comes to deciding where to learn a skill, there are many opinions. The people with these opinions haven't tried every single educational product (and are maybe even trying to sell you something), so it's hard to say what the best resource is. When it comes to picking the resource, I have no recommendation, other than to not stick with it if you don't like it.