In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory. The online setting has an additional requirement of maintaining a valid solution at any point in time. For solving these new problems, we propose algorithms with theoretical guarantees, evaluate them on several real-world datasets, and show that they give comparable performance to state-of-the-art offline algorithms that store the entire data in memory and take multiple passes over it.

The underlying assumption of recent federated learning (FL) paradigms is that local models usually share the same network architecture as the global model, which becomes impractical for mobile and IoT devices with different setups of hardware and infrastructure. A scalable federated learning framework should address heterogeneous clients equipped with different computation and communication capabilities. To this end, this paper proposes FedHM, a novel federated model compression framework that distributes the heterogeneous low-rank models to clients and then aggregates them into a global full-rank model. Our solution enables the training of heterogeneous local models with varying computational complexities and aggregates a single global model. Furthermore, FedHM not only reduces the computational complexity of the device, but also reduces the communication cost by using low-rank models. Extensive experimental results demonstrate that our proposed \system outperforms the current pruning-based FL approaches in terms of test Top-1 accuracy (4.6% accuracy gain on average), with smaller model size (1.5x smaller on average) under various heterogeneous FL settings.

Reinforcement learning (RL) agents are widely used for solving complex sequential decision making tasks, but still exhibit difficulty in generalizing to scenarios not seen during training. While prior online approaches demonstrated that using additional signals beyond the reward function can lead to better generalization capabilities in RL agents, i.e. using self-supervised learning (SSL), they struggle in the offline RL setting, i.e. learning from a static dataset. We show that performance of online algorithms for generalization in RL can be hindered in the offline setting due to poor estimation of similarity between observations. We propose a new theoretically-motivated framework called Generalized Similarity Functions (GSF), which uses contrastive learning to train an offline RL agent to aggregate observations based on the similarity of their expected future behavior, where we quantify this similarity using \emph{generalized value functions}. We show that GSF is general enough to recover existing SSL objectives while also improving zero-shot generalization performance on a complex offline RL benchmark, offline Procgen.

The Observation--Hypothesis--Prediction--Experimentation loop paradigm for scientific research has been practiced by researchers for years towards scientific discoveries. However, with data explosion in both mega-scale and milli-scale scientific research, it has been sometimes very difficult to manually analyze the data and propose new hypothesis to drive the cycle for scientific discovery. In this paper, we discuss the role of Explainable AI in scientific discovery process by demonstrating an Explainable AI-based paradigm for science discovery. The key is to use Explainable AI to help derive data or model interpretations as well as scientific discoveries or insights. We show how computational and data-intensive methodology -- together with experimental and theoretical methodology -- can be seamlessly integrated for scientific research. To demonstrate the AI-based science discovery process, and to pay our respect to some of the greatest minds in human history, we show how Kepler's laws of planetary motion and the Newton's law of universal gravitation can be rediscovered by (Explainable) AI based on Tycho Brahe's astronomical observation data, whose works were leading the scientific revolution in the 16-17th century. This work also highlights the important role of Explainable AI (as compared to Blackbox AI) in science discovery to help humans prevent or better prepare for the possible technological singularity that may happen in the future.

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020.

Preface: Three decades ago while working at Air Force Research Laboratory, I developed the first interactive Augmented Reality system, enabling users to reach out and touch a mixed world of real and virtual objects. I was so inspired by the reactions people had when they tried those early prototypes, I founded one of the first VR companies in 1993, Immersion Corp, and later founded the early AR company, Outland Research. Yes, I've been a believer for a long time. Looking forward, I expect augmented reality to become the platform of our lives, replacing smartphones as our primary means of accessing digital content. I still believe in the magical potential, but also fear the negative consequences. To paint a balanced picture of what our augmented lives will be like ten years from now, I've written the short narrative below. Like any fictional forecast it will not play out exactly like this, but I'm confident that the convergence of augmented reality and artificial intelligence will make much of this portrayal come true. It was a tiny room no larger than a walk-in closet. A small woman in a crisp white lab coat stood beside a large optometry machine, its smooth black surface covered in silver dials and knobs and levers. Flipping between settings she asked, "Better or worse?" "Better," rang a voice from behind the contraption. The woman pulled the machine forward, revealing Gordon Pines, squinting as the overhead lights suddenly came on. Balding with gray stubble, he looked older than his 68 years would suggest. That's because he was tired -- exhausted from the simple act of leaving his small apartment and venturing out into the busy city. Chicago had been his home for three decades but somehow it just didn't feel familiar anymore.

The deaf community is large and diverse, with members living all over the world. If you know sign language, you can communicate with a wide range of hearing, hard of hearing, and deaf people, including students in mainstream and deaf school or university programs, as well as deaf or hard of hearing citizens and business people in your community. Sign language is a type of nonverbal human communication in which the recipient receives information through hand motions. The shape of the hands, their posture, and how they move are all distinct features of each sign. When vocal communication is problematic, such as between speakers of mutually incomprehensible languages or when one or more would-be communicators is deaf, sign language can help.

Artificial Intelligence (AI) is something having an exponential effect on all we do. In recent years, the development has been optimizing processes and simplifying tasks in personal and professional aspects, and marketing is benefiting tremendously from this advance. Already three years ago, we were talking about how the digital revolution and Artificial Intelligence (AI) were going to change the rules forever. But why is marketing having such a significant advantage of this new way of "automated living?" One of the primary objectives of marketing is to understand consumer needs fully.

Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease. All the important libraries you would need to work on Machine learning lifecycle. Full-fledged course on Statistics so that you don't have to take another course for statistics, we cover it all. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go. Total 10 beginner to Advance level projects so that you can test your skills.