Ensemble methods have been widely used for improving the results of the best single classification model. Indeed, a large body of works have achieved better results mainly by applying one specific ensemble method. However, very few works analyze complex fusion schemes using heterogeneous ensemble strategies. This paper is three-fold: 1) It provides a tutorial of the most popular ensemble methods, 2) analyzes the best ensembles using MNIST as guiding thread and 3) shows that complex fusion architectures based on heterogeneous ensembles can be considered as a mode of taking benefit from diversity. We introduce a complex fusion design that achieves a new record in MNIST with only 10 misclassified images.

Real-time Linear Operator Construction and State Estimation with Kalman Filter Tsuyoshi Ishizone 1 Graduate School of Advanced Mathematical Sciences, Meiji University and Kazuyuki Nakamura Department of Interdisciplinary Mathematical Sciences, Meiji University JST, PRESTO Abstract Kalman filter is the most powerful tool for estimation of the states of the linear Gaussian system. In addition, used this method, expectation maximization algorithm can estimate the parameters of the model. Thus, we propose new method that can estimate the transition matrices and the states of the system in real-time. Applied to damped oscillation model, we have obtained extraordinary performance to estimate the matrices. Also, introduced localization and spatially uniformity to the method, we have demonstrated that our methods could reduce noise in high-dimensional spatiotemporal data. Moreover, this methodology has potential in areas such as weather forecast and vector field analysis. Keywords: state space model, noise reduction, flow analysis, online learning, weather forecast 1 INTRODUCTION A quick tool of noise reduction and short-term prediction is important for areas such as weather forecast and adjusting scanning probe microscope (SPM). In weather forecast, engineers need a speedy denoising method to utilize the result for instantaneous forecast.

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.

Classification algorithms to mine data stream have been extensively studied in recent years. However, a lot of these algorithms are designed for supervised learning which requires labeled instances. Nevertheless, the labeling of the data is costly and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without significant loss of model performance. Active learning is one of these paradigms, whose main objective is to build classification models that request the lowest possible number of labeled examples achieving adequate levels of accuracy. Therefore, this work presents the FASE-AL algorithm which induces classification models with non-labeled instances using Active Learning. FASE-AL is based on the algorithm Fast Adaptive Stacking of Ensembles (FASE). FASE is an ensemble algorithm that detects and adapts the model when the input data stream has concept drift. FASE-AL was compared with four different strategies of active learning found in the literature. Real and synthetic databases were used in the experiments. The algorithm achieves promising results in terms of the percentage of correctly classified instances.

In this article, we propose a multimodal perspective to diagrammatic representations by sketching a description of what may be tentatively termed the diagrammatic mode . We consider diagrammatic representations in the light of contemporary multimodality theory and explicate what enables diagrammatic representations to integrate natural language, various forms of graphics, diagrammatic elements such as arrows, lines and other expressive resources into coherent organisations. We illustrate the proposed approach using two recent diagram corpora and show how a multimodal approach supports the empirical analysis of diagrammatic representations, especially in identifying diagrammatic constituents and describing their interrelations.

Each semester, Associate Professor Virginia Vassilevska Williams tries to impart one fundamental lesson to her computer-science undergraduates: Math is the foundation of everything. Often, students come into Williams' class, 6.006 (Introduction to Algorithms), wanting to dive into advanced programming that power the latest, greatest computing techniques. Her lessons instead focus on how algorithms are designed around core mathematical models and concepts. "When taking an algorithms class, many students expect to program a lot and perhaps use deep learning, but it's very mathematical and has very little programming," says Williams, the Steven G. (1968) and Renee Finn Career Development Professor who recently earned tenure in the Department of Electrical Engineering and Computer Science. "We don't have much time together in class (only two hours a week), but I hope in that time they get to see a little of the beauty of math -- because math allows you to see how and why everything works together. It really is a beautiful thing."

Teachers: Could you use an extra 13 hours in your work week, or for your personal life? That might be possible in the future, according to a report published this week by McKinsey & Company "How Artificial Intelligence Will Impact K-12 Teachers." The report estimates that 20 to 40 percent of the tasks teachers spend time on--grading, lesson planning, general administration--could be outsourced to technology. But, the report notes, AI can't inspire students, resolve conflicts, or mentor and coach. Robots could free teachers up to focus on those more important--and more rewarding--tasks.

Machine learning is the future. As a machine learning developer, you surely want to succeed in your goals. That's where open-source tools for machine learning comes in. The machine learning open-source community is active. If you are into open-source, you will notice that there are plenty of machine learning resources.

"I took a few of your courses and you are an amazing teacher. Your courses have brought me up to speed on how to create databases and how to interact and handle Data Engineers and Data Scientists. I will be forever grateful." "By taking this course my perception has changed and now data science for me is more about data wrangling. Welcome to The Complete Course for Machine Learning Engineers.