Dear colleagues, The Technical University of Madrid (UPM) will once more organize the 'Madrid UPM Machine Learning and Advanced Statistics' summer school. The summer school will be held in Boadilla del Monte, near Madrid, from June 19th to June 30th. This year's edition comprises 12 week-long courses (15 lecture hours each), given during two weeks (six courses each week). Attendees may register in each course independently. No restrictions, besides those imposed by timetables, apply on the number or choice of courses.
Early registration is now *OPEN*. Extended information on course programmes, price, venue, accommodation and transport is available at the school's website: https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.dia.fi.upm.es%2FMLAS&data=05%7C01%7Cuai%40engr.orst.edu%7Cebc28f4db253459ba8ae08db1fde324f%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638138811436094830%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=C09G6ixDn3%2BKQreYFBnfwoaeN5VY9LQKKDjYE2TJMfk%3D&reserved=0 There is a 25% discount for members of Spanish AEPIA and SEIO societies. Please, forward this information to your colleagues, students, and whomever you think may find it interesting. Best regards, Pedro Larrañaga, Concha Bielza, Bojan Mihaljević and Laura Gonzalez Veiga. -- School coordinators. *** List of courses and brief description *** * Week 1 (June 19th - June 23rd, 2023) * 1st session: 9:45-12:45 Course 1: Bayesian Networks (15 h) Basics of Bayesian networks. Inference in Bayesian networks. Learning Bayesian networks from data. Real applications. Practical demonstration: R. Course 2: Time Series(15 h) Basic concepts in time series. Linear models for time series. Time series clustering. Practical demonstration: R. 2nd session: 13:45-16:45 Course 3: Supervised Classification (15 h) Introduction. Assessing the performance of supervised classification algorithms. Preprocessing. Classification techniques. Combining multiple classifiers. Comparing supervised classification algorithms. Practical demonstration: python. Course 4: Statistical Inference (15 h) Introduction. Some basic statistical tests. Multiple testing. Introduction to bootstrap methods. Introduction to Robust Statistics. Practical demonstration: R. 3rd session: 17:00 - 20:00 Course 5: Neural Networks and Deep Learning (15 h) Introduction. Learning algorithms. Learning in deep networks. Deep Learning for Images. Deep Learning for Text. Practical session: Jupyter notebooks in Python Anaconda with keras and tensorflow. Course 6: Bayesian Inference (15 h) Introduction: Bayesian basics. Conjugate models. MCMC and other simulation methods. Regression and Hierarchical models. Model selection. Practical demonstration: R and WinBugs. * Week 2 (June 26th - June 30th, 2023) * 1st session: 9:45-12:45 Course 7: Feature Subset Selection (15 h) Introduction. Filter approaches. Embedded methods. Wrapper methods. Additional topics. Practical session: R and python. Course 8: Clustering (15 h) Introduction to clustering. Data exploration and preparation. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and final advice. Practical session: R. 2nd session: 13:45-16:45 Course 9: Gaussian Processes and Bayesian Optimization (15 h) Introduction to Gaussian processes. Sparse Gaussian processes. Deep Gaussian processes. Introduction to Bayesian optimization. Bayesian optimization in complex scenarios. Practical demonstration: python using GPytorch and BOTorch. Course 10: Explainable Machine Learning (15 h) Introduction. Inherently interpretable models. Post-hoc interpretation of black box models. Basics of causal inference. Model-specific explanations: Bayesian networks. Other topics. Practical demonstration: R. 3rd session: 17:00-20:00 Course 11: Support Vector Machines and Regularized Learning (15 h) Introduction. SVM models. SVM learning algorithms. Regularized learning. Convex optimization with proximal methods. Practical session: Python Anaconda with scikit-learn. 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. Practical session: HTK. If you wish to stop receiving emails regarding the Madrid UPM Machine Learning and Advanced Statistics summer school, please reply to this email with the title STOP. _______________________________________________ uai mailing list uai@engr.orst.edu https://it.engineering.oregonstate.edu/mailman/listinfo/uai
