BASIC EDUCATION REQUIREMENT for Research Mathematical Statistician, GS-1529: Applicants must meet A or B below to satisfy the basic education requirement for Research Mathematical Statistician at all grade levels. A. Possess a degree that included 24 semester hours of mathematics and statistics, of which at least 12 semester hours were in mathematics and 6 semester hours were in statistics. B. A combination of education and experience -- at least 24 semester hours of mathematics and statistics, including at least 12 hours in mathematics and 6 hours in statistics, as shown in A above, plus appropriate experience or additional education. Courses in mathematical statistics or probability theory with a prerequisite of elementary calculus or more advanced courses will be accepted toward meeting the mathematics requirements, with the provision that the same course cannot be counted toward both the mathematics and the statistics requirement. Evaluation of Experience: The experience offered in combination with educational courses to meet the requirements in paragraph B above should include evidence of statistical work such as (a) sampling, (b) collecting, computing, and analyzing statistical data, and (c) applying known statistical techniques to data such as measurement of central tendency, dispersion, skewness, sampling error, simple and multiple correlation, analysis of variance, and tests of significance.

This course introduces the concepts of Artificial Intelligence and Machine learning. This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure.

By the end of the course you will have a comprehensive overview of the entire research, development and deployment lifecycle of a machine learning model, and understood the best coding practices, and things to consider to put a model in production. You will also have a better understanding of the tools available to you to deploy your models, and will be well placed to take the deployment of the models in any direction that serves the needs of your organization. What else should you know? This course will help you take the first steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure. But, there is a lot more to model deployment, like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow, as well as various testing paradigms such as shadow deployments that are not covered in this course.

This podcast series sets out to seek interesting profiles within the drone industry and discover unique viewpoints on industry developments and contributions!

This Course Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks. You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques.

If you're working with Natural Language Processing, knowing how to deploy a model is one of the most important skills you'll need to have. Model deployment is the process of integrating your model into an existing production environment. The model will receive input and predict an output for decision-making for a specific use case. There are different ways you can deploy your NLP model into production, like using Flask, Django, Bottle or other frameworks. But in today's article, you will learn how to build and deploy your NLP model with FastAPI.

In this thesis, we explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience. The free energy principle is a general mathematical theory of the necessary information-theoretic behaviours of systems which maintain a separation from their environment. A core postulate of the theory is that complex systems can be seen as performing variational Bayesian inference and minimizing an information-theoretic quantity called the variational free energy. The free energy principle originated in, and has been extremely influential in theoretical neuroscience, having spawned a number of neurophysiologically realistic process theories, and maintaining close links with Bayesian Brain viewpoints. The thesis is split into three main parts where we apply methods and insights from the free energy principle to understand questions first in perception, then action, and finally learning. Specifically, in the first section, we focus on the theory of predictive coding, a neurobiologically plausible process theory derived from the free energy principle under certain assumptions, which argues that the primary function of the brain is to minimize prediction errors. We focus on scaling up predictive coding architectures and simulate large-scale predictive coding networks for perception on machine learning benchmarks; we investigate predictive coding's relationship to other classical filtering algorithms, and we demonstrate that many biologically implausible aspects of current models of predictive coding can be relaxed without unduly harming the performance of predictive coding models which allows for a potentially more literal translation of predictive coding theory into cortical microcircuits. In the second part of the thesis, we focus on the application of methods deriving from the free energy principle to action. We study the extension of methods of'active inference', a neurobiologically grounded account of action through variational message passing, to utilize deep artificial neural networks, allowing these methods to'scale up' to be competitive with state of the art deep reinforcement learning methods.

The initiative is developing (1) national guidelines for AI education for K-12, (2) an online, curated resource Directory to facilitate AI instruction, and (3) a community of practitioners, researchers, resource and tool developers focused on the AI for K-12 audience. Check out the following information to learn about this initiative. A poster you can print, and an illustrative graphic. This poster is also available in multiple languages. We have icons available for each of the Five Big Ideas on the poster page under Resources.

IIT Madras has invited applications for a free online course called Introduction to Machine Learning on the NPTEL platform. The course, which is AICTE FDP approved, can be taken by senior undergraduate or postgraduate students pursuing their BE, MS, ME or even PhD. The course is 12 weeks long and will be conducted from 26 July to 15 October 2021. It would be most beneficial for students pursuing education in the domains of computer science and engineering, artificial intelligence, data science, programming and robotics. The course will be conducted by professor Balaraman Ravindran who is associated with the department of computer science at the Indian Institute of Technology Madras and is also a Mindtree Faculty Fellow.

In this Course you will learn and gain 6 new habits. Each habit will make big change in your Memorization Ability. Many people who have taken this course before were able to memorize the whole holy Quran short Time. Even some of them were able to memorize the whole Quran in short Time. This course helped myself and when I noticed the amazing results, I have decided to do this course publicly to help million of Muslims around the world.