In this paper, we study online convex optimization in dynamic environments, and aim to bound the dynamic regret with respect to any sequence of comparators. Existing work have shown that online gradient descent enjoys an $O(\sqrt{T}(1+P_T))$ dynamic regret, where $T$ is the number of iterations and $P_T$ is the path-length of the comparator sequence. However, this result is unsatisfactory, as there exists a large gap from the $\Omega(\sqrt{T(1+P_T)})$ lower bound established in our paper. To address this limitation, we develop a novel online method, namely adaptive learning for dynamic environment (Ader), which achieves an optimal $O(\sqrt{T(1+P_T)})$ dynamic regret. The basic idea is to maintain a set of experts, each attaining an optimal dynamic regret for a specific path-length, and combines them with an expert-tracking algorithm. Furthermore, we propose an improved Ader based on the surrogate loss, and in this way the number of gradient evaluations per round is reduced from $O(\log T)$ to $1$. Finally, we extend Ader to the setting that a sequence of dynamical models is available to characterize the comparators.

Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs. However, most inverse optimization algorithms are designed specifically in batch setting, where all the data is available in advance. As a consequence, there has been rare use of these methods in an online setting suitable for real-time applications. In this paper, we propose a general framework for inverse optimization through online learning. Specifically, we develop an online learning algorithm that uses an implicit update rule which can handle noisy data. Moreover, under additional regularity assumptions in terms of the data and the model, we prove that our algorithm converges at a rate of $\mathcal{O}(1/\sqrt{T})$ and is statistically consistent. In our experiments, we show the online learning approach can learn the parameters with great accuracy and is very robust to noises, and achieves a dramatic improvement in computational efficacy over the batch learning approach.

Are you looking for the Best SQL Certification Online Training for beginners? Here is the list of Best SQL Courses Online, Tutorial, & most respected SQL classes and SQL course review to help you impress your current or a potential employer. If you are a SQL developer or database administrator we've no doubt that you spent countless hours assisting your organization gaining quicker access to the data according to needs to solve complex problems and exploring new business opportunities. Learning SQL (Structured Query Language) tutorial is one of the best ways to improve your career prospects as it is one of the most in-demand tech skills. Salaries for junior level SQL Developers are upwards of $70,000 – $90,000 dollars a year!

Science fiction visions of the future show us AI built to replicate our way of thinking -- but what if we modeled it instead on the other kinds of intelligence found in nature? Robotics engineer Radhika Nagpal studies the collective intelligence displayed by insects and fish schools, seeking to understand their rules of engagement. In a visionary talk, she presents her work creating artificial collective power and previews a future where swarms of robots work together to build flood barriers, pollinate crops, monitor coral reefs and form constellations of satellites. Radhika Nagpal Taking cues from bottom-up biological networks like those of social insects, Radhika Nagpal helped design an unprecedented "swarm" of ant-like robots. Why you should listen With a swarm of 1,024 robots inspired by the design of ant colonies, Radhika Nagpal and her colleagues at Harvard's SSR research group have redefined expectations for self-organizing robotic systems.

Among thousands, 10 programming languages stand out for their job marketability and wide use. Anyone can learn it from his/her initial stage in the field of software development. A free alternative to pricey statistical software such as Matlab or SAS, over the last few years R has become the golden child of data science. Why You Should Learn Python Python is one of the top programming languages requested by companies in 2017 / 2018.

Get Robotics Certification taking the Online Robotics Degree Programs. However, you can get an online Robotics Degree from a lot of places like Coursera, Udemy, EDx, Futurelearn and so on. Description: One of the robotics tutorial for beginners to advanced where over 1000 students enrolled! So, learn Robotics online to open career opportunities and have fun to learn electronics focused on building robots automation! Description: An autonomous light-seeking an obstacle avoiding robot for Arduino Makers that want to learn the hard way.

HOy traemos a este espacio el último número el Vol 13, No 10 (2018) del International Journal of Emerging Technologies in Learning (iJET) This interdisciplinary journal aims to focus on the exchange of relevant trends and research results as well as the presentation of practical experiences gained while developing and testing elements of technology enhanced learning. So it aims to bridge the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Readers don't have to pay any fee. Vol 13, No 10 (2018) Table of Contents Papers Innovative English Classroom Teaching Based on Online Computer Technology in Rural Middle and Primary Schools Application of Brain Neural Network in Personalized English Education System Songlin Yang, Min Zhang A Generic Tool for Generating and Assessing Problems Automatically using Spreadsheets Maria Assumpció Rafart Serra, Andrea Bikfalvi, Josep Soler Masó, Jordi Poch Garcia A High Security Distance Education Platform Infrastructure Based on Private Cloud Jingtai Ran, Kepeng Hou, Kegang Li, Niya Dai Student Performance Prediction Model Based on Discriminative Feature Selection Haixia Lu, Jinsong Yuan An Eight-Layer Model for Mathematical Cognition Marios A. Pappas, Athanasios S. Drigas, Fotini Polychroni Design and Implementation of University Art Education Management System Based on JAVA Technology The Design and Application of Flip Classroom Teaching Based on Computer Technology Jia Li, Xiaoxia Zhang, Zijun Hu Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining Intelligent System for College English Listening and Writing Training Development of an Accounting Skills Simulation Practice System Based on the B/S Architecture Jianmei Liu, Rong Fu Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm Sujuan Jia, Yajing Pang Blended Learning Innovation Model among College Students Based on Internet Score Prediction Model of MOOCs Learners Based on Neural Network Yuan Zhang, Wenbo Jiang Design and Implementation of the Online Computer-Assisted Instruction System Based on Object-Oriented Analysis Technology Wenbo Zhou, Lei Shi, Jian Chen Matlab-Realized Visual A* Path Planning Teaching Platform Communication Jigsaw: A Teaching Method that Promotes Scholarly Communication A Tablet-Computer-Based Tool to Facilitate Accurate Self-Assessments in Third- and Fourth-Graders Denise Villanyi, Romain Martin, Philipp Sonnleitner, Christina Siry, Antoine Fischbach Short Papers Application of Blockchain Technology in Online Education Han Sun, Xiaoyue Wang, Xinge Wang The Grading Multiple Choice Tests System via Mobile Phone using Image Processing Technique Worawut Yimyam, Mahasak Ketcham Offline Support Model for Low Bandwidth Users to Survive in MOOCs International Journal of Emerging Technologies in Learning.

The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictive models of student learning behaviors such as some engagement or performance indicators. A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand. In this paper, we make the first attempt to solve the feature learning problem by taking the unsupervised learning approach to learn a compact representation of the raw features with a large degree of redundancy. Specifically, in order to capture the underlying learning patterns in the content domain and the temporal nature of the clickstream data, we train a modified auto-encoder (AE) combined with the long short-term memory (LSTM) network to obtain a fixed-length embedding for each input sequence. When compared with the original features, the new features that correspond to the embedding obtained by the modified LSTM-AE are not only more parsimonious but also more discriminative for our prediction task. Using simple supervised learning models, the learned features can improve the prediction accuracy by up to 17% compared with the supervised neural networks and reduce overfitting to the dominant low-performing group of students, specifically in the task of predicting students' performance. Our approach is generic in the sense that it is not restricted to a specific supervised learning model nor a specific prediction task for MOOC learning analytics.

In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoing courses, when the student outcomes are yet unknown, must rely on models trained from the historical data of previously offered courses. It is possible to transfer models, but they often have poor prediction performance. One reason is features that inadequately represent predictive attributes common to both courses. We present an automated transductive transfer learning approach that addresses this issue. It relies on problem-agnostic, temporal organization of the MOOC clickstream data, where, for each student, for multiple courses, a set of specific MOOC event types is expressed for each time unit. It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term. With these methods, we investigate the transferability of dropout prediction across similar and dissimilar MOOCs and compare with known methods. Results show improved model transferability and suggest that the methods are capable of automatically learning a feature representation that expresses common predictive characteristics of MOOCs.

We've all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; naturally, some people fear that they will become redundant -- depending on their job, some may be right. In this age of AI and ML, ambivalence is ripe; but something ironic has emerged: when it comes to advancing these cutting-edge technologies, the lack of human skills and knowledge is slowing innovation down. In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML -- it's second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.