This paper investigates the problem of designing data-driven stochastic Model Predictive Control (MPC) for linear time-invariant systems under additive stochastic disturbance, whose probability distribution is unknown but can be partially inferred from data. We propose a novel online learning based risk-averse stochastic MPC framework in which Conditional Value-at-Risk (CVaR) constraints on system states are required to hold for a family of distributions called an ambiguity set. The ambiguity set is constructed from disturbance data by leveraging a Dirichlet process mixture model that is self-adaptive to the underlying data structure and complexity. Specifically, the structural property of multimodality is exploit-ed, so that the first- and second-order moment information of each mixture component is incorporated into the ambiguity set. A novel constraint tightening strategy is then developed based on an equivalent reformulation of distributionally ro-bust CVaR constraints over the proposed ambiguity set. As more data are gathered during the runtime of the controller, the ambiguity set is updated online using real-time disturbance data, which enables the risk-averse stochastic MPC to cope with time-varying disturbance distributions. The online variational inference algorithm employed does not require all collected data be learned from scratch, and therefore the proposed MPC is endowed with the guaranteed computational complexity of online learning. The guarantees on recursive feasibility and closed-loop stability of the proposed MPC are established via a safe update scheme. Numerical examples are used to illustrate the effectiveness and advantages of the proposed MPC.

In today's Digital era, capability building and knowledge retention in an organization has changed. Among the wider demographic as well, people have varied ways of learning. Some prefer reading, others watching videos and yet others who prefer audio based podcasts etc. What is the best way to target this wide audience of keen learners and personalize the experience to make e-learning easily accessible and much more immersive and interesting? In this session about applying AI/ML to learning, we will look at how to tackle this problem and take learning into the next generation.

In this era of big data, there is an increasing need to develop and deploy algorithms that can analyze and identify connections in that data. Using machine learning (a subset of artificial intelligence) it is now possible to create computer systems that automatically improve with experience. This technology has numerous real-world applications including robotic control, data mining, autonomous navigation, and bioinformatics. This course features classroom videos and assignments adapted from the CS229 graduate course as delivered on-campus at Stanford in Autumn 2018 and Autumn 2019. In order to make the content and workload more manageable for working professionals, the course has been split into two parts, XCS229i: Machine Learning and XCS229ii: Machine Learning Strategy and Intro to Reinforcement Learning.

In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

In the Internet-of-Things (IoT) systems, there are plenty of informative data provided by a massive number of IoT devices (e.g., sensors). Learning a function from such data is of great interest in machine learning tasks for IoT systems. Focusing on streaming (or sequential) data, we present a privacy-preserving distributed online learning framework with multiplekernels (named DOMKL). The proposed DOMKL is devised by leveraging the principles of an online alternating direction of multipliers (OADMM) and a distributed Hedge algorithm. We theoretically prove that DOMKL over T time slots can achieve an optimal sublinear regret, implying that every learned function achieves the performance of the best function in hindsight as in the state-of-the-art centralized online learning method. Moreover, it is ensured that the learned functions of any two neighboring learners have a negligible difference as T grows, i.e., the so-called consensus constraints hold. Via experimental tests with various real datasets, we verify the effectiveness of the proposed DOMKL on regression and time-series prediction tasks.

The Personal MS(DS) is an initiative to customize the Data Science Masters roadmap according to one's interests hence providing complete autonomy to the learner. The intuition behind #thepersonalmsds is to upgrade skills without formally enrolling into a Master's program at a University - EshbanTheLearner/thepersonalMSDS-v2

It may sound like the revenge of structured data, but it's actually just a survey conducted by Kaggle Platform. The result of the 2019 Kaggle Machine Learning and Data Science Survey was made available here and that is the data that we have used to see what they could tell us about data scientists. I know this first figure may look like bad news for you if you're just getting started in the journey to become a data scientist. I don't want to give you any spoiler, but if you feel disapointed now, go check the last section called Education x Salary and it may calm you down again. Most of the data scientists who answerd the survey has indeed a Master's degree on their back.

Offered by Stanford University. Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses. In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines. CME Accreditation The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. View the full CME accreditation information on the individual course FAQ page.

The elements of AI is a free online course for everyone interested in learning what AI is, what is possible (and not possible) with AI, and how it affects our lives – with no complicated math or programming required. By completing the course you can earn a LinkedIn certificate. People in Finland can also earn 2 ECTS credits through the Open University. The course is available from May 14, 2018.

Zach uses a robot called a Swivl; Thomas uses one called an Owl. Both are types of telepresence robots or smart videoconferencing computers with microphones and speakers attached. Others stand in the classroom or even roll around. This technology has become increasingly popular in K-12 classrooms during the pandemic thanks to hybrid or blended learning models, where some students are in the classroom while others watch from home. The big difference between a robot and a conventional camera is that the robot follows action and sound -- spinning as much as 360 degrees, so students at home can see more than a static shot of the classroom.