We consider the problem of online multiclass classification with partial feedback, where an algorithm predicts a class for a new instance in each round and only receives its correctness. Although several methods have been developed for this problem, recent challenging real-world applications require further performance improvement. In this paper, we propose a novel online learning algorithm inspired by recent work on learning from complementary labels, where a complementary label indicates a class to which an instance does not belong. This allows us to handle partial feedback deterministically in a margin-based way, where the prediction margin has been recognized as a key to superior empirical performance. We provide a theoretical guarantee based on a cumulative loss bound and experimentally demonstrate that our method outperforms existing methods which are non-margin-based and stochastic.

In this paper, we investigate the impact of diverse user preference on learning under the stochastic multi-armed bandit (MAB) framework. We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms under the standard stochastic MAB setting can be reduced to a constant. Our intuition is that to achieve sub-linear regret, the number of times an optimal arm being pulled should scale linearly in time; when all arms are optimal for certain users and pulled frequently, the estimated arm statistics can quickly converge to their true values, thus reducing the need of exploration dramatically. We cast the problem into a stochastic linear bandits model, where both the users preferences and the state of arms are modeled as {independent and identical distributed (i.i.d)} d-dimensional random vectors. After receiving the user preference vector at the beginning of each time slot, the learner pulls an arm and receives a reward as the linear product of the preference vector and the arm state vector. We also assume that the state of the pulled arm is revealed to the learner once its pulled. We propose a Weighted Upper Confidence Bound (W-UCB) algorithm and show that it can achieve a constant regret when the user preferences are sufficiently diverse. The performance of W-UCB under general setups is also completely characterized and validated with synthetic data.

Hoy traemos a este espacio el último número, recién salido de la revista 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 14, No 02 (2019) Table of Contents Papers Multi-Dimensional Analysis to Predict Students' Grades in Higher Education Eslam Abou Gamie, Samir Abou El-Seoud, Mostafa Salama, Walid Hussein Implemented and Tested Conception Proposal of Adaptation Model for Adaptive Hypermedia Mehdi Tmimi, Mohamed Benslimane, Mohammed Berrada, Kamar Ouzzani Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models Mohamed El Fouki, Noura Aknin, Kamal Eddine El Kadiri Visualization Teaching of Deformation Monitoring and Data Processing based on MATLAB 3D Course Teaching Based on Educational Game Development Theory – Case Study of Game Design Course The Development and Performance Evaluation of Digital Museums Toward Second Classroom of Primary and Secondary School – Taking Zhejiang Education Technology Digital Museum as An Example Ying Zheng, Yuhui Yang, Huifang Chai, Mo Chen, Jianping Zhang Students' Beliefs Regarding the Use of E-portfolio to Enhance Cognitive Skills in a Blended Learning Environment Prakob Koraneekij, Jintavee Khlaisang Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System AlBara Khalifa, Tsuneo Kato, Seiichi Yamamoto The Different Roles of Help-Seeking Personalities in Social Support Group Activity on E-Portfolio for Career Development Suthanit Wetcho, Jaitip Na-Songkhla Short Papers A Review of Digital Skills of Malaysian English Language Teachers Mohd Zulhilmi Che Had, Radzuwan Ab Rashid International Journal of Emerging Technologies in Learning.

Etihad Airways, the national airline of the UAE, has announced a strategic partnership with Microsoft to launch the first ever in-house AI Academy in the region, which will revolutionise the way the airline serves its customers by upskilling its workforce, optimising operations and creating alternate revenue streams. As part of the AI Academy, all Etihad employees will be given access to an online training programme, and instructor led classes, to drive companywide AI literacy, empowering every employee to deliver more value to the company and its customers. Microsoft specialists will also conduct a series of AI business workshops and hands-on technical lab sessions to help identify business challenges that can be optimised with AI. Etihad is currently embarking on a digital transformation journey in order to enhance the capacity and quality of its services to the almost 20 million passengers it carries each year. "There is a simple reason that we are long-term partners with Microsoft – we think alike," said, Tony Douglas Chief Executive Officer, Etihad Aviation Group.

Machine learning is a growing area of computer science and several programming languages support ML framework and libraries. Among all of the programming languages, Python is the most popular choice followed by C, Java, JavaScript, and C#.

Stochastic gradient descent (SGD) is a popular and efficient method with wide applications in training deep neural nets and other nonconvex models. While the behavior of SGD is well understood in the convex learning setting, the existing theoretical results for SGD applied to nonconvex objective functions are far from mature. For example, existing results require to impose a nontrivial assumption on the uniform boundedness of gradients for all iterates encountered in the learning process, which is hard to verify in practical implementations. In this paper, we establish a rigorous theoretical foundation for SGD in nonconvex learning by showing that this boundedness assumption can be removed without affecting convergence rates. In particular, we establish sufficient conditions for almost sure convergence as well as optimal convergence rates for SGD applied to both general nonconvex objective functions and gradient-dominated objective functions. A linear convergence is further derived in the case with zero variances.

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate it on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SequenceR is able to perfectly predict the fixed line for 950/4711 testing samples, and find correct patches for 14 bugs in Defects4J. It captures a wide range of repair operators without any domain-specific top-down design.

In 1959, Arthur Samuel mentioned the words machine learning out of the blue to investigate the development of algorithms that can be utilized to forecast on data by conquering static programming instructions entirely to settle on predictions and choices based on data. Machine learning is utilized today in various computing works where the utilization of unequivocal programming and designing algorithms isn't practical like detection of a data breach by malevolent insiders or system intruders and so forth. The expanding demand for experts in machine learning amid a recent couple of years has increased interest to know the programming languages which one can use in machine learning. Microsoft-owned coding repository, GitHub has published a rundown of well-known programming languages utilized for machine learning. While Python keeps on holding the top position in the rundown, there are more languages that are bringing proficiency building machine learning algorithm than just Python.

Chatting with Roo is free and private, so go ahead and ask the things you don't want to ask out loud. Expert answers, every time Roo's answers are backed by professional health educators from Planned Parenthood, the most trusted provider of sexual education. Roo is built around the questions teens are actually asking. That thing you've always wondered about? Roo gets a little bit smarter every time you ask a question.

This book explains how human learning is promoted by applying artificial intelligence to education. Before that, let's first look back on how information technology including artificial intelligence contributed to education. Various technologies have been developed to make it easier for learners to learn and to create an environment where teachers can more easily teach. An example of this is called e-learning or intelligent tutoring systems (ITS). ITS was developed using a rule-based system which is an initial result of artificial intelligence. In the process, user models for learners called learner models and educational contents have been improved.