[DMANET] XVIII Madrid UPM Machine Learning and Advanced Statistics Summer School (June 8th - June 19th, 2026) - Early registration reminder

Dear colleagues, We would like to remind you that early registration for the Madrid UPM Machine Learning and Advanced Statistics summer school is open until May 31, 2026. The 2026 edition comprises 12 week-long courses (15 lecture hours each), given over two weeks. Attendees may register in each course independently, subject to timetable compatibility. KEY INFORMATION Dates: June 8-19, 2026 Venue: Boadilla del Monte, near Madrid Early registration: open until May 31, 2026 Website: https://dia.fi.upm.es/MLAS Discount: 25% for members of the Spanish AEPIA and SEIO societies COURSES ======= WEEK 1: June 8-12, 2026 ---------------------- 09:45-12:45 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: Metaheuristics for Optimization (15 h) Single-solution metaheuristics. Evolutionary algorithms. Algorithms based on estimation of distributions. Other population-based algorithms. Multi-objective optimization using evolutionary algorithms. 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: Reinforcement Learning (15 h) Introduction. Dynamic programming methods. Temporal-difference learning. Policy gradient methods. Offline reinforcement learning. Practical demonstration: R. 17:00-20:00 Course 5: Deep Learning (15 h) Introduction. Learning algorithms. Learning in deep networks. Deep learning for computer vision. Deep learning for language. Practical session: Python notebooks with Google Colab, Keras, PyTorch and Hugging Face Transformers. 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 15-19, 2026 ----------------------- 09:45-12:45 Course 7: Causality (15 h) Introduction. Causal graphs. Mediation analysis. Instrumental variables. Counterfactual fairness. Practical sessions: R. 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. 13:45-16:45 Course 9: Gaussian Processes, Bayesian Deep Learning and Bayesian Optimization (15 h) Introduction to Gaussian processes. Sparse Gaussian processes. Deep Gaussian processes. Bayesian deep learning. Introduction to Bayesian optimization. Practical demonstration: Python using GPyTorch, PyTorch and BoTorch. Course 10: Explainable Machine Learning (15 h) Introduction. Inherently interpretable models. Post-hoc interpretation of black box models. Basics of causal inference. Beyond tabular and i.i.d. data. Other topics. Practical demonstration: Python with Google Colab. 17:00-20:00 Course 11: Generative AI (15 h) Introduction to the course. Neural networks and deep learning. Generative AI for images. Generative AI for language. Hands-on session: PyTorch, VAEs, GANs, diffusion models, LLMs, aligning a generative LLM, using an open-source image generation model. Course 12: AI for the Health Domain (15 h) Introduction to AI in healthcare. AI to conversational agents and drug repurposing. From manifolds to foundation models. AI for real-world clinical data. Please forward this information to colleagues, students, and anyone who may find it interesting. Best regards, Pedro Larrañaga, Concha Bielza, Bojan Mihaljević and Laura Gonzalez Veiga. -- School coordinators. ********************************************************** * * Contributions to be spread via DMANET are submitted to * * DMANET@zpr.uni-koeln.de * * Replies to a message carried on DMANET should NOT be * addressed to DMANET but to the original sender. The * original sender, however, is invited to prepare an * update of the replies received and to communicate it * via DMANET. * * DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET) * http://www.zaik.uni-koeln.de/AFS/publications/dmanet/ * **********************************************************