A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced. Random forests are used as marginal models for each response variable and, as novel contribution of the present work, the dependence between the multiple response variables is modeled by a generative neural network. This combined modeling approach of random forests, corresponding empirical marginal residual distributions and a generative neural network is referred to as RafterNet. Multiple datasets serve as examples to demonstrate the flexibility of the approach and its impact for making probabilistic forecasts.

Building predictive models is the main objective of machine learning (ML), a profession that combines statistics and software development. The world has undergone a rapid transition thanks to machine learning. We need to understand how to interpret the data and draw conclusions because it is expanding exponentially. We must constantly learn new things to remain at the top of our industry. One of my favorite methods is to pick the brains of more intelligent people, preferably for free.

You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, R and Predictive Modeling, right? You've found the right Machine Learning course! Check out the table of contents below to see what all Machine Learning models you are going to learn. How will this course help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

You're looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in Python, right? You've found the right Classification modeling course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. Why should you choose this course?

You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Python, right? You've found the right Linear Regression course! A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. How this course will help you? Why should you choose this course?

With a focus on safety and the opportunity to greatly enhance operations and the quality of research and learning, educational institutions could see significant gains by implementing computer vision with real-time federated analytics. Computer vision is revolutionizing many industries but is still making inroads into education. That's not surprising, given the historically tight budgets for many educational institutions. As the technology advances and becomes more mainstream in the commercial world, colleges and universities are more likely to be the first adopters in the education realm. With a camera infrastructure already in place on most education campuses, along with adequate district, campus and departmental networks, much of the infrastructure needed for computer vision is already in place.

In 2006 Xi'an Jiaotong-Liverpool University (XJTLU) was created by the University of Liverpool and Xi'an Jiaotong University – a top ten university in China. Offering a unique international education experience, XJTLU brings together excellent research practice and expertise from both institutions and gives students the skills and knowledge they need to secure careers in a global marketplace. XJTLU now has over 25,000 enrolled students in both Suzhou and Liverpool in the UK, with plans to grow to about 28,000 students by 2025. There are currently about 2,000 staff, among which about 1,000 academic staff, with an almost even split between citizens of the People's Republic of China and international passport holders. XJTLU offers our undergraduates and postgraduates over 100 programmes with a diverse spectrum of courses.

Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-answer. In particular, a promising approach adapts frozen autoregressive language models pretrained on Web-scale text-only data to multi-modal inputs. In contrast, we here build on frozen bidirectional language models (BiLM) and show that such an approach provides a stronger and cheaper alternative for zero-shot VideoQA. In particular, (i) we combine visual inputs with the frozen BiLM using light trainable modules, (ii) we train such modules using Web-scraped multi-modal data, and finally (iii) we perform zero-shot VideoQA inference through masked language modeling, where the masked text is the answer to a given question. Our proposed approach, FrozenBiLM, outperforms the state of the art in zero-shot VideoQA by a significant margin on a variety of datasets, including LSMDC-FiB, iVQA, MSRVTT-QA, MSVD-QA, ActivityNet-QA, TGIF-FrameQA, How2QA and TVQA. It also demonstrates competitive performance in the few-shot and fully-supervised setting. Our code and models are publicly available at https://github.com/antoyang/FrozenBiLM.

Pollutant exposure during gestation is a known and adverse factor for birth and health outcomes. However, the links between prenatal air pollution exposures and educational outcomes are less clear, in particular the critical windows of susceptibility during pregnancy. Using a large cohort of students in North Carolina, we study the link between prenatal daily $\mbox{PM}_{2.5}$ exposure and 4th end-of-grade reading scores. We develop and apply a locally adaptive and highly scalable Bayesian regression model for scalar responses with functional and scalar predictors. The proposed model pairs a B-spline basis expansion with dynamic shrinkage priors to capture both smooth and rapidly-changing features in the regression surface. The model is accompanied by a new decision analysis approach for functional regression that extracts the critical windows of susceptibility and guides the model interpretations. These tools help to identify and address broad limitations with the interpretability of functional regression models. Simulation studies demonstrate more accurate point estimation, more precise uncertainty quantification, and far superior window selection than existing approaches. Leveraging the proposed modeling, computational, and decision analysis framework, we conclude that prenatal $\mbox{PM}_{2.5}$ exposure during early and late pregnancy is most adverse for 4th end-of-grade reading scores.

Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolated data silos by cooperatively training models among organizations without breaching privacy and security. However, FL faces heterogeneity from various aspects, including data space, statistical, and system heterogeneity. For example, collaborative organizations without conflict of interest often come from different areas and have heterogeneous data from different feature spaces. Participants may also want to train heterogeneous personalized local models due to non-IID and imbalanced data distribution and various resource-constrained devices. Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL. In this survey, we comprehensively investigate the domain of heterogeneous FL in terms of data space, statistical, system, and model heterogeneity. We first give an overview of FL, including its definition and categorization. Then, We propose a precise taxonomy of heterogeneous FL settings for each type of heterogeneity according to the problem setting and learning objective. We also investigate the transfer learning methodologies to tackle the heterogeneity in FL. We further present the applications of heterogeneous FL. Finally, we highlight the challenges and opportunities and envision promising future research directions toward new framework design and trustworthy approaches.