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[Apologies for Cross-Posting] =========================================================== Announce: Two Short-courses on Bayesian Modelling and Computation and Statistical Machine Learning. Lecturers: Sujit Sahu (University of Southampton, UK) and Dr Sourish Das (Chennai Mathematical Institute) Date: June 11-15, 2018 Venue: University of Southampton, UK. =========================================================== Course 1: Introduction to Bayesian Hierarchical Modelling and Computation, June 11-12, 2018. The first short-course is aimed at applied scientists (with good graduate degrees) who are thinking of using Bayesian methods and would like to receive a gentle introduction with a large practical component using R and WinBuGS. No previous knowledge of Bayesian methods is necessary. However, familiarity with standard probability distributions (normal, binomial, Poisson, gamma) and standard statistical methods such as multiple regression will be assumed. Theory lectures on the Bayes theorem, elements of Bayesian inference, choice of prior distributions and introduction to MCMC will be followed by hands-on experience using R and the WinBUGS software. =========================================================== Course 2: Course 2: Statistical Machine Learning. June 13-15, 2018. This course will provide an overview of basic ideas in statistical machine learning. The topics to be covered include: Supervised learning, classification, algorithms and unsupervised learning. The course begins with detailed discussion of supervised learning. It will discuss the sub-topics of regression where the usual topics of multicollinearity, variable selection, regularisation, LASSO prior, Ridge prior, Elastic Net prior will be illustrated with many examples. This will be followed by the discussion of non-linear regression where we will also consider the topics of Gaussian Process Prior Regression. The topic of Classification will be discussed with special emphasis on the naive Bayes classifier, Discriminant Analysis, logistic regression, Decision Tree, Support Vector Machine, Random Forest, Perceptron Learning, Neural Network and Deep Learning. Next, we will discuss various popular algorithms such as the Gradient Descent, Stochastic Gradient Descent and Back Propagation. Unsupervised learning is the last major topic to be discussed in this course. Here we will introduce the K-means clustering, principal component analysis and latent Dirichlet analysis. Participants should have a good understanding of data handling and analysis. In addition, basic familiarity with standard statistical methods such as multiple linear regression and computing will be required. Attending the preceding two-day course on Bayesian statistics and MCMC will help prepare for this course, although that is not an absolute pre-requisite. This course will also have a large practical component where hands on training using the R-software will be provided to the participants. Methods will be illustrated using several practical examples from finance, system bilogy and social sciences. R-code and data sets will also be provided at the beginning of the courses. If you are unsure about the suitability of your background for the course, please email Prof Sujit Sahu ([log in to unmask]) who can advise. Please visit the course page: http://www.soton.ac.uk/~sks/2018course/ for further information and a tentative timetable. For registration please visit: http://go.soton.ac.uk/94y part 1 http://go.soton.ac.uk/94x part 2 =========================================================== You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.