This paper studies the decentralized optimization and learning problem where multiple interconnected agents aim to learn an optimal decision function defined over a reproducing kernel Hilbert (RKH) space by jointly minimizing a global objective function, with access to locally observed data only. As a non-parametric approach, kernel learning faces a major challenge in distributed implementation: the decision variables of local objective functions are data-dependent with different sizes and thus cannot be optimized under the decentralized consensus framework without any raw data exchange among agents. To circumvent this major challenge and preserve data privacy, we leverage the random feature (RF) approximation approach to map the large-volume data represented in the RKH space into a smaller RF space, which facilitates the same-size parameter exchange and enables distributed agents to reach consensus on the function decided by the parameters in the RF space. For fast convergent implementation, we design an iterative algorithm for Decentralized Kernel Learning via Alternating direction method of multipliers (DKLA). Further, we develop a COmmunication-censored KErnel learning (COKE) algorithm to reduce the communication load in DKLA. To do so, we apply a communication-censoring strategy, which prevents an agent from transmitting at every iteration unless its local updates are deemed informative. Theoretical results in terms of linear convergence guarantee and generalization performance analysis of DKLA and COKE are provided. Comprehensive tests with both synthetic and real datasets are conducted to verify the communication efficiency and learning effectiveness of COKE.

Peer Assessment is a task of analysis and commenting on student's writing by peers, is core of all educational components both in campus and in MOOC's. However, with the sheer scale of MOOC's & its inherent personalised open ended learning, automatic grading and tools assisting grading at scale is highly important. Previously we presented survey on tasks of post classification, knowledge tracing and ended with brief review on Peer Assessment (PA), with some initial problems. In this review we shall continue review on PA from perspective of improving the review process itself. As such rest of this review focus on three facets of PA namely Auto grading and Peer Assessment Tools (we shall look only on how peer reviews/auto-grading is carried), strategies to handle Rogue Reviews, Peer Review Improvement using Natural Language Processing. The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey-2.

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour - the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.

Learning Management Systems (LMS) and Educational Data Mining (EDM) are two important parts of online educational environment with the former being a centralised web-based information systems where the learning content is managed and learning activities are organised (Stone and Zheng,2014) and latter focusing on using data mining techniques for the analysis of data so generated. As part of this work, we present a literature review of three major tasks of EDM (See section 2), by identifying shortcomings and existing open problems, and a Blumenfield chart (See section 3). The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey. The coverage statistics and review matrix of the survey are as shown in Figure 1 & Table 1 respectively. Acronym expansions are added in the Appendix Section 4.1.

UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!

MADS East 2019 was a two-day conference in December that gave attendees endless opportunities to expose themselves to new ideas in the space of data science for marketing. Some of this year's conference perks included: tables for one-on-one networking, a half-an-hour off the record roundtable with 7 industry leaders, two unique tracks per day, buffet-style lunches, breakfasts, snacks, a refreshing break for cocktails at the Opening Night Party, and NYC Times Square views. This article is my summary of the Day 1 presentations I was able to attend, including lessons and reminders from the speakers. Aside from staying up to date on industry trends, MADS East has also proven itself a valuable opportunity for data and marketing people who are looking to engage with professionals of varying career levels. I was expecting to be the only individual with little background in data or extended industry experience present, but to my surprise, there was a decent balance between early, mid and late-career attendees.

IT4Innovations and M Computers would like to invite you to three full day NVIDIA Deep Learning Institute certified training courses to learn more about Artificial Intelligence (AI) and High Performance Computing (HPC) development for NVIDIA GPUs. The first half day is an introduction by IT4Innovations and M Computers about the latest state of the art NVIDIA technologies. We also explain our services offered for AI and HPC, for industrial and academic users. The introduction will include a tour though IT4Innovations' computing center, which hosts an NVIDIA DGX-2 system and the new Barbora cluster with V100 GPUs. The first full day training course, Fundamentals of Deep Learning for Computer Vision, is provided by IT4Innovations and gives you an introduction to AI development for NVIDIA GPUs.

Artificial intelligence offers a way to solve that problem.

January 25, 2020 - The AI revolution is upon us. How much is Croatia lagging behind, and are we going to do something about it? But even if we start those processes, where would we be in comparison to the rest of the world? What are other countries already doing and what should we be aware of? Fortunately, a fear of missing out is spreading around the globe or at least among some countries.

Python is a very popular multi-paradigm programming language. Object-oriented programming and structured programming are fully supported in this, and many of its features support functional programming and aspect-oriented programming – for that matter. This easy-to-understand course aims to teach everyone the basics of Python Language, learning outcomes, benefits of learning Python, advantages of Python etc. You will learn where to use Python Language and know about who would actually use it in their daily office lives. You will also learn the comparative parameters of python with other programming languages, in the world – with a highlight on popularity and frameworks of Python.