We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning problems. Acknowledging the distributional uncertainty in learning from limited samples, the min-max formulations introduce an adversarial inner player to explore unseen covariate data. The resulting Distributionally Robust Optimization (DRO) formulations, which include Wasserstein DRO formulations (our main focus), are specified using optimal transportation phenomena. Upon describing how these infinite-dimensional min-max problems can be approached via a finite-dimensional dual reformulation, the tutorial moves into its main component, namely, explaining a generic recipe for optimally selecting the size of the adversary's budget. This is achieved by studying the limit behavior of an optimal transport projection formulation arising from an inquiry on the smallest confidence region that includes the unknown population risk minimizer. Incidentally, this systematic prescription coincides with those in specific examples in high-dimensional statistics and results in error bounds that are free from the curse of dimensions. Equipped with this prescription, we present a central limit theorem for the DRO estimator and provide a recipe for constructing compatible confidence regions that are useful for uncertainty quantification. The rest of the tutorial is devoted to insights into the nature of the optimizers selected by the min-max formulations and additional applications of optimal transport projections.

This webinar series will be hosted by the International Research Centre in Artificial Intelligence (IRCAI) and supported by UNESCO and Knowledge 4 All Foundation, to present the Fellowship to develop datasets and strengthen capacities and innovation potential for Low Resource African Languages project that is composed of research in natural language processing, open dataset creation and publishing, and the development of an interface between policy and technology sphere. The project delivered three main components from research in natural language processing, dataset creation, and policy creation: 1. Fellowship for African AI researchers focused on African languages, based on previously IDRC and Knowledge 4 All Foundation funded work on language datasets. This work contributes to a roadmap for better integration of African languages on digital platforms in aid of lowering the barrier for African participation in the digital economy, 2. Improvement of the representation of AI research carried out on African languages by creating resources for a variety of NLP tasks and in a variety of African languages that will enable good, data-driven results in AI research, 3. Attract an African community of native speakers as contributors of language resources and language technology tools to adopt and support Masakhane NLP, a platform for sharing, maintaining and making use of language resources and tools; establishing widely agreed benchmarks for NLP tasks and stimulating competition between methods and systems, 4. Be used as a model case to inform African evidence-based policymaking concerning Artificial Intelligence and will be included in UNESCO’s AI Decision maker’s Essential to inform policymakers. Find more information at IRCAI Webinar Series

If you are interested in learning to use NumPy, Pandas, Machine Learning, and more from the comfort of your home then you have landed to the right course. In this course, you will be taught all about using Python for data science and machine learning in the best possible manner. The instructor will explain how you can use spark for big data analysis in detail. Then you will get a chance to understand how to implement machine learning algorithms. Going further, you will get a chance to understand how to use Matplotlib for python plotting.

Understand stock market fundamentals Understand the Modern Portfolio Theory Understand stochastic processes and the famous Black-Scholes mode Understand Monte-Carlo simulations Understand Value-at-Risk (VaR) You should have an interest in quantitative finance as well as in mathematics and programming! This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main. Markowitz-model is the first step.

Data science and machine learning can be practiced with varying degrees of efficiency and productivity. Let's imagine somebody is teaching a "Productive Data Science" course or writing a book about it -- using Python as the language framework. What should the typical expectations be from such a course or book? The course/book should be intended for those who wish to leapfrog beyond the standard way of performing data science and machine learning tasks and utilize the full spectrum of the Python data science ecosystem for a much higher level of productivity. Readers should be taught how to look out for inefficiencies and bottlenecks in the standard process and how to think beyond the box.

You probably interact with computers daily, but let's be specific about what we mean when we talk about computers and programming. Programming tells the computer how to receive, process, and then store this data. When someone writes a program, that person gives the computer a set of commands to follow. Programming, at its core, takes a big problem and breaks it down into smaller and smaller problems until they are small enough that we can tell the computer to solve the problem. We are going to discuss what programming languages are, what are the main differences, and where you can learn them.

The AI Ethics Certification course teaches right and wrong, if such a thing exists, in the context of the artificial intelligence industry. This three-section training starts by asking "What is ethics?" We'll discuss its history, different philosophies, ethics in business, and learn the five most common principles. Second, ethics as it pertains specifically to AI with interviews from founders across the globe. We will examine commonly cited principles from governments and leaders and equate them to the five traditional pillars.

Remember, programming has its own jargon. For example, you will have to deal with such a concept as cycles no matter what language you use. Home programming education is now more affordable thanks to educational technology offering a wide range of courses and programs. During the pandemic, when people have more free time, these resources will help both novice and experienced programmers. The former will get acquainted with the basics of coding, and the latter will be able to hone their professional skills.

PyTorch is a deep learning library developed by Facebook to develop machine learning models for NLP, Computer Vision and AI, to name a few. It was developed by Facebook's Artificial Intelligence Research Group and is used to run deep learning frameworks. PyTorch is an excellent framework for entering the actual machine learning and neural network building process. It is ideal for complex neural networks such as RNNNs, CNNs, LSTMs and neural networks that you want to design for a specific purpose. PyTorch is a very different kind of deep learning library (dynamic vs. static) that was adopted by many researchers if not most, and it's flexible approach and easy-to-understand style have won over newcomers and industry veterans alike.

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.