ABU DHABI, 16th October, 2019 (WAM) -- Abu Dhabi today announced the establishment of the Mohamed bin Zayed University of Artificial Intelligence, MBZUAI, the first graduate level, research-based AI university in the world. MBZUAI will enable graduate students, businesses, and governments to advance the artificial intelligence field. The University is named after His Highness Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, who has long advocated for the UAE's development of human capital through knowledge and scientific thinking to take the nation into the future. MBZUAI will introduce a new model of academia and research to the field of AI, providing students and faculty access to some of the world's most advanced AI systems to unleash its potential for economic and societal development. The announcement was made at a press conference at the University campus in Masdar City and was immediately followed by the first meeting of the MBZUAI Board of Trustees.

What is optimization Some real-life situations where we need to optimize an objective The mathematical formalism of optimization How discrete optimization (Combinatorics) differs from continuous optimization Different approaches to solve a Combinatorics problem, including-- The simplest, perfect but slow'Brute Force' method. One of the fastest and practicable'Greedy' heuristic.A look-ahead mechanism to refine the greedy approach. Travelling Salesman Problem Other generic problems in discrete optimization, like the Knapsack Problem How metaheuristic approaches compare to heuristic solutions The nature-inspired class of metaheuristic approaches Ant Colony Optimization: its basis, modus operandi, algorithm and flow chart The R library to implement Ant Colony Optimization and other heuristic solutions Examples of Travelling Salesman Problems solved through different approaches ESSENTIAL: A moderate knowledge of Mathematics (High School level) BOOSTER: Familiarity with some programming language (preferably R) BOOSTER: Interest in solving puzzles and games involving logic BOOSTER: Basic knowhow on what Data Science is about Discrete Optimization is something all of us use in our daily activities when say, we order at a restaurant, decide which subject to study, take up a new activity… or look for a change. It comprises of choosing between alternatives that best suit some objective we have in mind. When such things are formalized, i.e. the objective and the ability of each choice to fulfill that objective are quantified, we get a mathematical expression of the problem we would optimize.

With student outcomes being a key performance metric for higher education institutes, many are turning to leverage data to glean insights and drive improved outcomes. "For many institutions, student performance has a direct impact on rankings. AI can be a tool to help better manage outcomes and ensure continued innovation to optimize operations and enhance student engagements, as it reduces resource-intensive work among faculty and administrative staff," said Larry Nelson, Regional General Manager – Education, Microsoft Asia. "In fact, we found that 3 out 4 education leaders agree that AI will be able to drive competitiveness in the next 3 years. However, only 32% of education institutions in Asia Pacific have embarked on their AI journey," added Nelson.

Meta-Learning describes the abstraction to designing higher level components associated with training Deep Neural Networks. The term "Meta-Learning" is thrown around in Deep Learning literature frequently referencing "AutoML", "Few-Shot Learning", or "Neural Architecture Search" when in reference to the automated design of neural network architectures. Emerging from comically titled papers such as "Learning to learn by gradient descent by gradient descent", the success of OpenAI's rubik's cube robotic hand demonstrates the maturity of the idea. Meta-Learning is the most promising paradigm to advance the state-of-the-art of Deep Learning and Artificial Intelligence. OpenAI set the AI world on fire by demonstrating ground-breaking capabilities of a robotic hand trained with Reinforcement Learning.

Ever since its origin in post-war research, AI has been subject to profound hyperbole, rapturous prognostications, and projected nightmares. In 2019, things have once again reached fever pitch in what science board co-chair and External Professor Melanie Mitchell wryly notes is a hype cycle that routinely ripples through her fellow computer scientists and those who fund them. Her illuminating new book, Artificial Intelligence: A Guide for Thinking Humans, lays bare the inner workings of these potent tools, exposing their realistic limits and patiently detailing our deployment errors. It is a solid history of how we got from pocket calculators to facial recognition and self-driving cars, a lucid tour of how these systems operate, and a tempered read on just how far we have to go before we're obsolete. Mitchell, a professor of computer science at Portland State University, has spent decades studying AI and writes with the measured understanding of someone who has lived on the volcano.

What do you want to find out or discover using your data? Do you have the appropriate data to analyze? Data is key to any data science and machine learning task. Data comes in different flavors such as numerical data, categorical data, text data, image data, sound data, and video data. The predictive power of a model depends on the quality of data used in building the model.

I have read a book or some posts on machine learning. I have watched some of the Coursera machine learning course. I still don't know how to get started… How do you get started in machine learning? The most common question I'm asked by developers on my newsletter is: How do I get started in machine learning? I honestly cannot remember how many times I have answered it. In this post, I lay out all of my very best thinking on this topic. You are a developer and you're interested in getting into machine learning. You read some blog posts.

Are you interested in the field of Deep Learning? Here is the short and useful Review of Deep Learning A-Z Hands-On Artificial Neural Networks. If you are in the intermediate level people who know the basics of Deep Learning and Machine Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning. This is one of the Best Seller courses on Udemy where students enrolled more than 157K with 21K reviews and 4.5 average star rating. With this top-selling Deep Learning tutorial, you will learn how to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts.

Every weekday, this feed brings you our latest talks in audio format. Hear thought-provoking ideas on every subject imaginable -- from Artificial Intelligence to Zoology, and everything in between -- given by the world's leading thinkers and doers. This collection of talks, given at TED and TEDx conferences around the globe, is also available in video format.

A BSTRACT To analyze deep ReLU network, we adopt a student-teacher setting in which an over-parameterized student network learns from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD). First, we prove that when the gradient is zero (or bounded above by a small constant) at every data point in training, a situation called interpolation setting, there exists many-to-one alignment between student and teacher nodes in the lowest layer under mild conditions. This suggests that generalization in unseen dataset is achievable, even the same condition often leads to zero training error. Second, analysis of noisy recovery and training dynamics in 2-layer network shows that strong teacher nodes (with large fan-out weights) are learned first and subtle teacher nodes are left unlearned until late stage of training. As a result, it could take a long time to converge into these small-gradient critical points. Our analysis shows that over-parameterization plays two roles: (1) it is a necessary condition for alignment to happen at the critical points, and (2) in training dynamics, it helps student nodes cover more teacher nodes with fewer iterations. Although networks with even one-hidden layer can fit any function (Hornik et al., 1989), it remains an open question how such networks can generalize to new data. Different from what traditional machine learning theory predicts, empirical evidence (Zhang et al., 2017) shows more parameters in neural network lead to better generalization. How over-parameterization yields strong generalization is an important question for understanding how deep learning works. In this paper, we analyze multi-layer ReLU networks by adopting teacher-student setting. The fixed teacher network provides the output for the student to learn via SGD. The student is over-parameterized (or over-realized): it has more nodes than the teacher. Therefore, there exists student weights whose gradient at every data point is zero. Here, we want to study the inverse problem: With small gradient at every training sample, can the student weights recover the teachers'? If so, then the generalization performance can be guaranteed if the training converges to such critical points. In this paper, we show that this so-called interpolation setting (Ma et al., 2017; Liu & Belkin, 2018; Bassily et al., 2018) leads to alignment: under certain conditions, each teacher node is provably aligned with at least one student node in the lowest layer. The condition is simply that the teacher node is observed by at least one student node, i.e., teacher's ReLU boundary lies in the activation region of that student. Therefore, more over-parameterization increases the probability of teachers being observed and thus being aligned. Furthermore, in 2-layer case, those student nodes that are not aligned with any teacher have zero contribution to the output and can be pruned.