Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and temporal dependencies. While most of these models focus on obtaining accurate point forecasts, they do not characterize the prediction uncertainty. In this work, we consider the time-series data as a random realization from a nonlinear state-space model and target Bayesian inference of the hidden states for probabilistic forecasting. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings. Thorough experimentation on several real world time-series datasets demonstrates that our approach provides better characterization of uncertainty while maintaining comparable accuracy to the state-of-the art point forecasting methods.

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.

We will start with Python Installation and a few basics of Python. Once you reach here you can start the new journey to learn domain-specific python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras for machine learning. By the end of the course, you'll be able to apply in confidence for Python programming jobs with the right skills which you will learn in this course. Here's what a few students have told us about the Python programming course after going through it "This course is so recommended to anyone who wants to learn python. It clearly teaches you several important things even experts fail to deliver. It also teaches so many different ways and how to tackle some interview questions. Very thorough and easy to understand. "That was a very thorough and informative course.

Our roundtable event will showcase 4 tech startups that have AI or Data at the core of their business. Each will pitch for approximately ten minutes, followed by the opportunity for investors to ask questions of the management team and dig a little deeper into the investment opportunity. Should any catch your attention we'll put you in touch directly with the founder. Each business has a bulletin page on our website with full information on the deal. Current businesses confirmed are: MyCustomerLens QIARK TUBR Alqami Opening the event we have Jennifer Stirrup. Jen is the Founder and CEO of Data Relish. She is a recognized leading authority in AI and Business Intelligence Leadership, a Fortune 100 global speaker, and has been named as one of the Top 50 Global Data Visionaries, one of the Top Data Scientists to follow on Twitter and one of the most influential Top 50 Women in Technology worldwide. Jen holds postgraduate degrees in AI and Cognitive Science and has authored books in data and artificial intelligence has been featured on CBS Interactive and the BBC as well as other well-known podcasts, such as Digital Disrupted, Run As Radio and her own Make Your Data Work webinar series. Jen will be welcoming you by putting the AI and data in context with the wider market, outlining the possibilities and where it’s headed, sharing success stories, highlighting what investors might want to look out for, and also focusing on the challenges within the sector.

Find helpful learner reviews, feedback, and ratings for Generating New Recipes using GPT-2 from Coursera Project Network. Read stories and highlights from ... In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes, and split it into a training and validation set. You will learn how to use the HuggingFace library to fine-tune a deep, generative model, and specifically how to train such a model on Google Colab. Finally, you will learn how to use GPT-2 effectively to create realistic and unique recipes from lists of ingredients based on the aforementioned dataset. This project aims to teach you how to fine-tune a large-scale model, and the sheer magnitude of resources it takes for these models to learn.

Getting Started with Quantum Machine Learning The most exciting advancement just may be in the field of Artificial Intelligence and Machine Learning. Quantum computers can theoretically speed up matrix ... In this 2-hour long project-based course, you will learn basic principles of how machine learning can benefit from work, and how this can be implemented in Python using the Pennylane library by Xanadu. Quantum Computing represents a completely new paradigm in the computing realm, posed to revolutionize entire industries and bring amazing new innovations as they are used for purposes such as material design, pharmaceutical design, genetic and molecular simulations, and weather simulations. The most exciting advancement just may be in the field of Artificial Intelligence and Machine Learning. Quantum computers can theoretically speed up matrix multiplications and process massive amounts of data very quickly, and thus may represent a paradigm shift in AI and ML.

About this Course 18,922 recent views This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions. The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques.

Penn State has launched its first degree program in artificial intelligence, a master's degree that is offered online and designed to provide a professional, technical education in developing and deploying AI and machine learning. Penn State is now accepting applications for the Master of Professional Studies in Artificial Intelligence program, which is being offered by the engineering division of the Penn State Great Valley School of Graduate Professional Studies online through Penn State World Campus. The first courses will start in January 2022. Penn State's first academic offering in AI comes at a time of soaring growth in the job market and of increasing presence of AI in everyday products and services, said Colin Neill, a professor of software and systems engineering who is the director of the new program. The World Economic Forum estimates that AI will lead to the creation of 97 million new jobs by 2025.

Function optimization is a foundational area of study and the techniques are used in almost every quantitative field. Importantly, function optimization is central to almost all machine learning algorithms, and predictive modeling projects. As such, it is critical to understand what function optimization is, the terminology used in the field, and the elements that constitute a function optimization problem. In this tutorial, you will discover a gentle introduction to function optimization. A Gentle Introduction to Function Optimization Photo by USFS, Interior West FIA, some rights reserved.

South Eastern Regional College (@S_ERC) is hosting an online international conference to highlight the need for businesses to identify employee digital skills gaps and provide relevant digital skills training for employees in preparation for the move to Artificial Intelligence (AI) technology within the work environment. The Reskilling Revolution in the Artificial Intelligence Era: The Case for Digital Upskilling is online 10am – 1.00pm GMT, Thursday 17 June. However, more and more businesses are also reaping the benefits of AI whether that's a small business replacing paper-based processes with digital, using drones, virtual reality or augmented reality or a total transformational change. The impact for employees could be devastating, with concerns over job role and security, but by providing relevant digital upskilling training, employers can retain valuable employees minimising uncertainty and job losses." Speakers will include leading experts from the field of Artificial Intelligence who will share their knowledge and expertise.