The platform has the ability of facial recognition for the learners with 90% accuracy which is especially helpful in cheating prevention. Moreover, the platform detects student focus and engagement and alerts the instructors in case of inactivity.

In this paper we consider an online learning version of the Facility location problem where users need to be served one at a time in a sequence of trials. The goal is to select, at each trial, a subset of a given set of sites, and then pay a loss equal to their total "opening cost" plus the minimum "connection cost" for connecting the user to one of the sites in the subset. More precisely, we are given a set of N sites. At the beginning of each trial, an opening cost and a connection cost for the arriving user are associated with each site and are unknown. At each trial, the learner has to select a subset of sites and incurs a loss given by the minimum connection cost over the selected sites plus the sum of the opening costs of all selected sites. After each subset selection, the opening and connection costs of all sites are revealed. To solve this problem, we design and rigorously analyse an algorithm which belongs to the class of online learning algorithms that make use of the Exponentiated gradient method [15]. We measure, and rigorously analyse, the performance of our method by comparing its cumulative loss with that of any fixed subset of sites.

Artificial Intelligence (AI) is an evolving technology which is thriving among every business group in the world. As many as 20 per cent of jobs are likely to be AI-based jobs in most companies, according to McKinsey global institute. But what is artificial intelligence and how can it be useful for your field, if you have been thinking about this, we have got you covered. Here is a list of courses that can help young professionals of any stream boost up their skill-set. These courses are imparted online, are short-term, flexible, and can be a good utilisation of the lockdown period.

Deep Learning Prerequisites: The Numpy Stack in Python (V2+), Preview this course The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence BESTSELLER Created by Lazy Programmer Inc.  English [Auto-generated] Free Coupon Dicount Online Courses Udemy

Free Coupon Discount Preview this course Introduction to AI, Machine Learning and Python basics, Learn to understand Artificial Intelligence and Machine Learning algorithms, and learn the basics of Python Programming

We consider an online learning problem where the learner interacts with a Markov decision process in a sequence of episodes, where the reward function is allowed to change between episodes in an adversarial manner and the learner only gets to observe the rewards associated with its actions. We allow the state space to be arbitrarily large, but we assume that all action-value functions can be represented as linear functions in terms of a known low-dimensional feature map, and that the learner has access to a simulator of the environment that allows generating trajectories from the true MDP dynamics. Our main contribution is developing a computationally efficient algorithm that we call MDP-LinExp3, and prove that its regret is bounded by $\widetilde{\mathcal{O}}\big(H^2 T^{2/3} (dK)^{1/3}\big)$, where $T$ is the number of episodes, $H$ is the number of steps in each episode, $K$ is the number of actions, and $d$ is the dimension of the feature map. We also show that the regret can be improved to $\widetilde{\mathcal{O}}\big(H^2 \sqrt{TdK}\big)$ under much stronger assumptions on the MDP dynamics. To our knowledge, MDP-LinExp3 is the first provably efficient algorithm for this problem setting.

Dr. Andrew Ng is a globally recognized leader in artificial intelligence. He was until recently chief scientist at Baidu, where he led the company's approximately 1,300-person AI group and was responsible for driving its global AI strategy and infrastructure. He was also the founding lead of the Google Brain team. In addition, Dr. Ng is co-chairman and cofounder of Coursera, the world's leading MOOC (massive open online course) platform, and an adjunct professor of computer science at Stanford University. He has authored or coauthored over 100 research papers in machine learning, robotics, and related fields.

Create a computer vision program with python and OpenCV.

Introduction to Machine Learning:- What is Machine Learning?,

Sometime ago, the world's most affable and recognizable AI leader, Andrew Ng launched a specialization called AI for medicine through his MOOC institution, deeplearning.ai. I have always been a big fan of Andrew Ng, and it was he who had introduced me to the world of machine learning through his grainy Youtube videos of Stanford lectures back in 2012. I was very excited that finally, Andrew Ng has finally turned his attention to the critical shortage of AI experts in the medical field . Truth be told, AI in the medical world has not seen as much progress as other domains like personalized advertisements, recommendations, autonomous driving etc. There are lot of complex issues like data privacy, small sample sizes etc. which I would prefer to discuss in depth in another post.