Statistics: Estimators, asymptotic theory, and random variable convergence 2 with Michal Fabinger скачать в хорошем качестве

Statistics: Estimators, asymptotic theory, and random variable convergence 2 with Michal Fabinger

We're excited to host lectures on Statistics. They are a part of a 2022 lecture series that aims to build a solid foundation of statistics knowledge for the participants. These 2 lectures by Michal Fabinger cover core aspects of statistical models: estimators, their large-sample properties, and different types of convergence of sequences of random variables. The concepts are introduced in an intuitive yet rigorous way. 📌 To sign up for the whole lecture series, please fill out this form: https://form.typeform.com/to/rep1RuEc The material should later help the participants understand scientific articles that use probability theory and statistics. Such knowledge is useful both for machine learning and data science practitioners and for those on an academic path (undergraduates, graduate students, postdocs, or faculty members). The content is similar to the corresponding course at the Acalonia school. 📌 Topics discussed: the topics include the convergence of sequences of random variables in distribution, in probability, in r-th mean, and almost sure convergence, estimators, consistency of estimators, asymptotic distributions of estimators, and bias of estimators, the law of large numbers, and the central limit theorem. 👉 Lecturer: Michal Fabinger,   / fabinger   👉 Bio: Michal is the founder of the Acalonia school (acalonia.com, formerly tokyodatascience.com), which aims to build an education system for a world where location does not matter. The school provides a straightforward way for talented people from developed and developing countries to improve their skills for their current jobs, get new knowledge-demanding jobs, or get admitted to graduate schools. The Fair Play Tuition system (acalonia.com/fair-play) makes this possible even for those who currently lack finances. Michal's research is in physics and economics, with the corresponding Ph.D. training completed at Stanford and Harvard. At the University of Tokyo and the Pennsylvania State University, Michal taught courses on Deep Learning, Data Science, Statistics, Asset Pricing, International Trade, International Finance, and Development Economics. ========================= MLT (Machine Learning Tokyo) site: https://www.mlt.ai/ twitter:   / __mlt__   meetup: https://www.meetup.com/Machine-Learni... linkedin:   / mltai   github: https://github.com/Machine-Learning-T... slack: https://machinelearningtokyo.slack.com