Dear colleagues, The Technical University of Madrid (UPM) will once more organize the summer school on 'Advanced Statistics and Data Mining'. The summer school will be held in Madrid, between June 23rd and July 4th. This year's programme comprises 12 courses divided into 2 weeks. Attendees may register in each course independently. Early registration is now *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website: http://www.dia.fi.upm.es/ASDM Please, send this information to your colleagues, students, and whoever may find it interesting. Best regards, Pedro Larranaga, Concha Bielza, Bojan Mihaljevic and Laura Anton-Sanchez. -- School coordinators. *** List of courses and brief description *** * Week 1 (June 23rd - June 27th, 2014) * 1st session: 9:30 - 12:30 Course 1: Bayesian Networks (15 h) Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Course 2: Time Series(15 h) Basic concepts in time series. Descriptive methods for time series. Linear models for time series. Extensions. 2nd session: 13:30 - 16:30 Course 3: Supervised Pattern Recognition (15 h) Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Course 4: Bayesian Inference (15 h) Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. 3rd session: 17:00 - 20:00 Course 5: Neural Networks and Deep Learning (15 h) Introduction. Training algorithms. Learning and Optimization. MLPs in practice. Deep Networks. Course 6: Feature Subset Selection (15 h) Introduction. Filter approaches. Wrapper methods. Embedded methods. Advanced topics. Practical session. * Week 2 (June 30th - July 4th, 2014) * 1st session: 9:30 - 12:30 Course 7: Statistical Inference(15 h) Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Course 8: Bayesian Classifiers (15 h) Discrete predictors. Gaussian Bayesian networks-based classifiers. Other Bayesian classifiers. Bayesian classifiers for: positive and unlabeled data, semi-supervised learning, data streams, temporal data. 2nd session: 13:30 - 16:30 Course 9: Text Mining (15 h) Introduction. Fundamentals. Language Modeling. Text Classification. Information Extraction. Course 10: Unsupervised Pattern Recognition (15 h) Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advise. 3rd session: 17:00 - 20:00 Course 11: Support Vector Machines and Convex Optimization (15 h) Introduction. SVM models. SVM learning algorithms. Convex non differentiable optimization. Course 12: Hidden Markov Models (15 h) Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs.