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CEMMAP Masterclass: Optimal Transport and Applications to Econometrics
By Institute for Fiscal Studies
These lectures will introduce the optimal transport (OT) toolbox, with two applications in econometrics. The first one will pertain to the estimation of matching models. We start by introducing the discrete OT problem and its entropic regularization, and inverse OT, as well as its estimation using generalized linear models. The second application will deal with quantile methods. The one-dimensional OT problem will be discussed as well as its connections with the notions of quantile and rank is then covered. Connection with quantile regression will be discussed and the ‘vector quantile regression’ problem will then be introduced. Part I Introduction (3h) S1. Monge-Kantorovich duality (1h30) S2. Computational optimal transport (1h30) https://www.math-econ-code.org/optimal-assignment [https://www.math-econ-code.org/optimal-assignment] Part II OT and matching models (3h) S3. Matching with Transferable Utility and random utility (1h30) https://www.math-econ-code.org/regularized-optimal-transport [https://www.math-econ-code.org/regularized-optimal-transport] S4. Estimation of matching models (1h30) https://www.math-econ-code.org/matching-estimation [https://www.math-econ-code.org/matching-estimation] Part III OT and quantiles (2h) S5. 1D optimal transport and quantiles (1h) https://www.math-econ-code.org/one-dimensional-assignment [https://www.math-econ-code.org/one-dimensional-assignment] S5. Connection with quantile regression (1h) https://www.math-econ-code.org/quantile-regression [https://www.math-econ-code.org/quantile-regression]
Stress Management
By Mpi Learning - Professional Learning And Development Provider
Stress can affect your ability to think clearly and rationally and to perform well. This session aims to help you better deal with stress by sharing key insights and strategies to reduce stress and optimize your work-life balance, particularly as we face uncertainties in the economy, the workplace and the environment.
Harness the energy (In-House)
By The In House Training Company
Harness the energy is an explorative workshop introducing the notion of utilising and intuitively listening to the energy shared between a group of people. Whether we realise it or not, we all give off certain energies at any time. These energies can affect change, sometimes positively, sometimes negatively. This workshop enhances understanding of the power of our own energy transmissions through the use of compliciteÌ-based theatre techniques and exercises. This workshop will help participants become active and aware listeners, able to work better collaboratively to produce strong results. It will demonstrate the power of our own individual energy and the positive impact we can have when we learn to utilise the energy beneficially in different situations.
Fundamentals of E&P Data Management
By Asia Edge
ABOUT THIS VIRTUAL INSTRUCTOR LED TRAINING (VILT) The energy industry has started its journey to be more data centric by embracing the industry 4.0 concept. As a result, data management - which was considered until recently as a back-office service to support geoscience, reservoir management, engineering, production and maintenance - is now given the spotlight! To become an active stakeholder in this important transition in E&P data management, it is necessary to understand the new technical opportunities offered by the Cloud, Artificial Intelligence and how data governance can pave the way towards more reliable and resilient processes within E&P domain. Several key questions that need to be addressed: 1. Why place more focus on data assets? 2. Is data management just about serving geoscientists or engineers with fresh data? 3. What is the value of data management in the E&P sector for decision making? 4. How to convince the data consumers that the data we provide is reliable? 5. Is the data architecture of my organization appropriate and sustainable? The purpose of this 5 half-day Virtual Instructor Led Training (VILT) course is to present the data challenges facing the energy organizations today and see how they practically solve them. The backbone of this course is based on the DAMA Book of Knowledge for Data Management. The main data management activities are described in sequence with a particular focus on recent technological developments. Training Objectives Upon completion of this VILT course, the participants will be able to: * Understand why the data asset is now considered as a main asset by energy organizations * Appreciate the importance of data governance and become an active stakeholder of it * Understand the architecture and implementation of data structure in their professional environment * Get familiarized with the more important data management activities such as data security and data quality * Integrate their subsurface and surface engineering skills with the data managements concepts This VILT course is unique on several points: * All notions are explained by some short presentations. For each of them, a set of video, exercises, quizzes will be provided to help develop an engaging experience between the trainer and the participants * A pre-course questionnaire to help the trainer focus on the participants' needs and learning objectives * A detailed reference manual * A lexicon of terms for data-management * Limited class size to encourage the interactivity Target Audience This VILT course is intended for: * Junior/new data managers * Geoscientists * Reservoir engineers * Producers * Maintenance specialists * Construction specialists * Human resources * Legal Course Level * Basic or Foundation Training Methods The VILT course will be delivered online in 5 half-days consisting 4 hours per day, with 2 breaks of 10 minutes per day. Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total). Trainer Your expert course leader is a geologist by education who has dedicated his career to subsurface data management services. In 2016, he initiated a tech startup dedicated to Data Management using Artificial Intelligence (AI) tools. He is heavily involved in developing business plans, pricing strategies, partnerships, marketing and SEO, and is the co-author of several Machine Learning publications. He also delivers training on Data Management and Data Science to students and professionals. Based in France, he was formerly Vice President, Sales & Marketing at CGG where he was in charge of the Data Management Services strategy, Sales Manager at Spie O&G Services where he initiated the Geoscience technical assistance activities and Product Manager of interactive seismic inversion software design and marketing at Paradigm. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information about post training coaching support and fees applicable for this. Accreditions And Affliations
Why Should You Learn Machine Learning Its Significance, Working, and Roles
By garyv
Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune [https://www.sevenmentor.com/machine-learning-course-in-pune.php] The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune [https://www.sevenmentor.com/machine-learning-course-in-pune.php] Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune [https://www.sevenmentor.com/machine-learning-course-in-pune.php]
