It's hard to ignore the cultural and organizational impact that Artificial Intelligence (AI) has had over us. Most organizations today have realized the impact of AI, and are doing all that they can to participate in and help facilitate the growth of the technology. For those who know the nuances of AI and the metrics involved in it, Deep Learning and Machine Learning may not look like challenging terms. But, for those who are new to AI, these terms might be hard to understand. To understand the complications organizations face when adopting machine learning, we must first fully understand the difference between deep learning and machine learning.

Students are expected to be self-motivated, curious and enthusiastic about machine learning on graphs. You'll get the most out of this course by completing the (moderate time commitment) coursework, so make sure you have the free time and energy needed for that. The course will have lectures every two weeks, for four lectures, taking a total of two months to complete. Each two week cycle will begin with an interactive online lecture in a Google Hangout. This will be followed with a piece of coursework, provided as a template Google Colab notebook. A week after the lecture will be a tutorial session, where the students and tutor(s) will get together and discuss the coursework and what challenges students are having.

Artificial Intelligence may be the answer to a great set of issues that are apparent worldwide. AI would reform the human's daily life and bring new solutions to many industries. AI is deeply penetrating into the daily lives of people but still, there is a lot of untapped potential of Artificial Intelligence, especially towards the humanitarian cause. Scientist and AI experts are working to help solve some of the most important social and economic issues of our day. One of the most common problems for developing countries is to tackle poverty and AI can play a vital role.

Working with BIG DATA requires a particular suite of data analytics tools and advanced techniques, such as machine learning (ML). Many of these tools are readily and freely available in R. This full-day session will provide participants with a hands-on training on how to use data analytics tools and machine learning methods available in R to explore, visualize, and model big data. The first half of our training session will focus on organizing (manipulating and summarizing) and visualizing (both statically and dynamically) big data in R. The second half will involve a series of short lectures on ML techniques (decision trees, random forests, and support vector machines), as well as hands-on demonstrations applying these methods in R. Examples will be drawn from the OECD's Programme for International Student Assessment (PISA). Participants will get opportunities to work through several hands-on lab sessions throughout the day.

Pattern-based, modular ontologies have several beneficial properties that lend themselves to FAIR data practices, especially as it pertains to Interoperability and Reusability. However, developing such ontologies has a high upfront cost, e.g. reusing a pattern is predicated upon being aware of its existence in the first place. Thus, to help overcome these barriers, we have developed MODL: a modular ontology design library. MODL is a curated collection of well-documented ontology design patterns, drawn from a wide variety of interdisciplinary use-cases. In this paper we present MODL as a resource, discuss its use, and provide some examples of its contents.

This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market. In the first chapter we are going to talk about the basic graph algorithms.

Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even under model mismatch and with adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference? In this paper, we show that this is indeed the case for some variational inference (VI) algorithms. We propose new online, tempered VI algorithms and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that our result should hold more generally and present empirical evidence in support of this. Our work in this paper presents theoretical justifications in favor of online algorithms that rely on approximate Bayesian methods.

Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r.t. the primary task is not known a priori, the success of MTL models depends on the correct choice of these auxiliary tasks and also a balanced mixing ratio of these tasks during alternate training. These two problems could be resolved via manual intuition or hyper-parameter tuning over all combinatorial task choices, but this introduces inductive bias or is not scalable when the number of candidate auxiliary tasks is very large. To address these issues, we present AutoSeM, a two-stage MTL pipeline, where the first stage automatically selects the most useful auxiliary tasks via a Beta-Bernoulli multi-armed bandit with Thompson Sampling, and the second stage learns the training mixing ratio of these selected auxiliary tasks via a Gaussian Process based Bayesian optimization framework. We conduct several MTL experiments on the GLUE language understanding tasks, and show that our AutoSeM framework can successfully find relevant auxiliary tasks and automatically learn their mixing ratio, achieving significant performance boosts on several primary tasks. Finally, we present ablations for each stage of AutoSeM and analyze the learned auxiliary task choices.

It can be convenient to use a standard computer vision dataset when getting started with deep learning methods for computer vision. Standard datasets are often well understood, small, and easy to load. They can provide the basis for testing techniques and reproducing results in order to build confidence with libraries and methods. In this tutorial, you will discover the standard computer vision datasets provided with the Keras deep learning library. How to Load and Visualize Standard Computer Vision Datasets With Keras Photo by Marina del Castell, some rights reserved.

Previously published on LSE Business Review. Machine learning is poised to pave the way for many exciting opportunities for businesses, but there are many hurdles to be crossed before getting to the finishing line. Many organisations are still struggling with legacy systems and are slow to invest in more advanced technologies. But the more pressing issue at hand, one that has been an ongoing problem for the technology sector, is the short supply of qualified talent to match what is a fast-moving and demanding industry. By design, machine learning is experimental and often unpredictable – a lot of exploration is required before organisations can even begin to make sense of the data and which machine learning algorithms will work best. While the unpredictable nature of machine learning is understandably daunting, many organisations have yet to fully grasp what is required to effectively deploy and manage it.