We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods. Manifold models arise in a wide variety of modern machine learning problems, and our goal is to help understand the effectiveness of various implicit and explicit dimensionality-reduction methods that exploit manifold structure. Our first key contribution is to establish a novel nonasymptotic version of the Weyl law from differential geometry. From this we are able to show that certain spaces of smooth functions on a manifold are effectively finite-dimensional, with a complexity that scales according to the manifold dimension rather than any ambient data dimension. Finally, we show that given (potentially noisy) function values taken uniformly at random over a manifold, a kernel regression estimator (derived from the spectral decomposition of the manifold) yields minimax-optimal error bounds that are controlled by the effective dimension.

Adaptive gradient methods such as RMSProp and Adam use exponential moving estimate of the squared gradient to compute coordinate-wise adaptive step sizes, achieving better convergence than SGD in face of noisy objectives. However, Adam can have undesirable convergence behavior due to unstable or extreme adaptive learning rates. Methods such as AMSGrad and AdaBound have been proposed to stabilize the adaptive learning rates of Adam in the later stage of training, but they do not outperform Adam in some practical tasks such as training Transformers. In this paper, we propose an adaptive learning rate principle, in which the running mean of squared gradient is replaced by a weighted mean, with weights chosen to maximize the estimated variance of each coordinate. This gives a worst-case estimate for the local gradient variance, taking smaller steps when large curvatures or noisy gradients are present, which leads to more desirable convergence behavior than Adam. We prove the proposed algorithm converges under mild assumptions for nonconvex stochastic optimization problems, and demonstrate the improved efficacy of our adaptive averaging approach on image classification, machine translation and natural language understanding tasks. Moreover, our method overcomes the non-convergence issue of Adam in BERT pretraining at large batch sizes, while achieving better test performance than LAMB in the same setting. The code is available at https://github.com/zhuchen03/MaxVA.

In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer under this setting, as they forget previously acquired knowledge. To address this problem, effective methods exploit past data stored in an episodic memory while expanding the final classifier nodes to accommodate the new classes. In this work, we substitute the expanding classifier with a novel fixed classifier in which a number of pre-allocated output nodes are subject to the classification loss right from the beginning of the learning phase. Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model. Experiments with public datasets show that the proposed approach is as effective as the expanding classifier while exhibiting novel intriguing properties of the internal feature representation that are otherwise not-existent. Our ablation study on pre-allocating a large number of classes further validates the approach.

In this article, I identify extraordinary unmet learner needs and address them with a free offering: my business-oriented machine learning course series, which is designed to fulfill those needs – three vendor-neutral courses that deliver material critical for both techies and business leaders. If you or members of your team would benefit from taking the course series, see how to access it for free here. Your team needs it, your boss demands it, and your career loves it. But today's number-crunching craze tends to, tragically, overlook one key point: Of all the ingredients that are key to success with machine learning, the one that's most often missing isn't about technology or data. Many business leaders do know that machine learning can't succeed in optimizing operations without a proven management process guiding the project – but data scientists tend to focus on one thing and one thing only: hands-on practice with analytics.

Mentoring The 3AI Mentorus provides existing members an opportunity to interact with the Top Industry AI & Analytics Leaders in a group setting and on a 1:1 basis. Further, apart from building and upskilling knowledge and skills, the program also offers the members career guidance sessions, landscape of AI & analytics roles and job expectations, personalized career roadmap exercise and enabling members build relationships along with expanding their networks. Webinars, Seminars and Conferences Members can participate in Webinars, Seminars & Conferences and get an opportunity to build their knowledge base, gain deep expertise in their respective industry segments & functions, Get inspired by meeting leaders and learn from their experiences, get access to contextualized learning opportunities, insights on career progression and exchanging ideas and viewpoints from industry experts. Knowledge Insights The research library provides the members with an exhaustive and vast array of topical AI & Analytics blogs, videos, articles and newsletters. It helps in building contextualized learning by providing information about deep technology and domain areas leveraged by AI & Analytics.

Offered by IBM. Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis. This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries. Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.

Introduction The smartest companies today fully leverage data analytics for strategic decision making.The IBM Chief Analytics Office, a division of IBM Corporate Headquarters, combines business knowledge with big data to direct the future of IBM's strategic transformation. Do you want to drive significant change within a leading global company and influence C-suite level decisions? The IBM Chief Analytics Office is an elite analytics consulting team that is tasked by IBM executives to pursue our most complex strategic issues. Our work is a combination of data science and management consulting.Through state of the art technology, and the power of artificial intelligence we strive to improve traditional business decision-making and processes. Your Role and Responsibilities This position is an internal Staff Data Scientist Intern role with the Chief Analytics Office.

This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. Teaching yourself Python machine learning can be a daunting task if you don't know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics. It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters. A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category. While the title sounds like another introductory book on machine learning algorithms, the content is anything but.

The face of the education system has undergone a sea change in recent years. The present-day educational structure is competitive, challenging, and needs to be capable of meeting international benchmarks. The emerging technologies, such as artificial intelligence, are changing our lives as they are being put to different purposes. And just like other areas, AI is disrupting and creating an impact on the education system as well. AI is making long strides in the academic world, turning the traditional methods of imparting knowledge into a comprehensive system of learning with the use of simulation and augmented reality tools.

At FutureLearn we work in short sprints & regularly share, reflect on and iterate on our work. This helps us focus on shipping small, iterative changes and responding quickly to changing business or user needs. We care about work/life balance and supporting learning at work. The Data Platform Team builds and maintains tooling and infrastructure that supports decision making processes across the business and enables product improvements by providing a complete and consistent view of our business data. Our tech stack consists of an ETL process written in Ruby and managed by Airflow which sources data from our production database (MySQL), our email provider (Sendgrid), application logs, and other operational data sources.