. "Artificial Intelligence"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Statistical foundations of machine learning"@en . . "6" . "In this course, we Introduce the basics of Machine Learning from a statistical perspective. The focus of this course is on supervised learning, but other learning paradigms are also studied. The following topics will be addressed:\n\n1. The Learning Problem - 2. Is Learning Feasible? - 3. The Linear Model - 4. Error and Noise - 5. Training versus Testing - 6. Theory of Generalization - 7. The Vapnik-Chervonenkis Dimension - 8. Bias-Variance Tradeoff - 9. Neural Networks - 10. Overfitting - 11. Regularization - 12. Validation - 13. Support Vector Machines - 14. Kernel Methods - 15. Bayesian learning - 16. Reinforcement learning.\nGENERAL COMPETENCIES\r\nIntroduce the basics of Machine Learning from a statistical perspective. The student has to be able to 1) understand machine learning techniques, 2) formally prove theoretical guarantees about machine learning, 3) implement these techniques in Python, 4) apply these techniques to benchmark and real-world problems, and 5) evaluate the performance of machine learning techniques.\r\n\r\n• Knowledge and insight: After successful completion of the course the student should have insight into which problems can benefit from machine learning techniques and how to apply these techniques to the problem at hand. The student will gain insight in the studied methodologies and be able to reason about model complexities and learning guarantees.\r\n\r\n• Use of knowledge and insight: The student should be able to apply machine learning techniques and to tune the parameters of the chosen algorithm. The use of python will enable the student to write programs to solve problems. The exercise sessions and practical exam project will challenge students to solve research questions that consider both synthetic and real-world data.\r\n\r\n• Judgement ability: The student should be able to judge the qualities of the different machine learning techniques and their results on the problem at hand.\r\n\r\n• Communication: The student should be able to communicate with experts about machine learning problems. The student should also be able to report and to present the results of his or her experiments to both specialists and non-specialists. The practical exam project will challenge students to collaborate with their peers and communicate their results effectively." . . "Presential"@en . "FALSE" . . "Master of Science in Physics and Astronomy"@en . . "https://images.communicate.vub.ac.be/Web/VUB/%7Be03fbc44-87f1-488a-badd-e287777c0353%7D_WE_oplBrochure_MB_EN_Physics-Astronomy_8P.pdf?utm_medium=email&utm_source=eloqua&utm_content=MARCOM%20REKRUTERING%20brochure%20download%20ENG&%3Cutm_campaign= https://www.vub.be/en/studying-vub/all-study-programmes-vub/bachelors-and-masters-programmes-vub/master-in-physics-and-astronomy/program/master/master-physics-and-astronomy-minor-research\n" . "120"^^ . "Presential"@en . "The Master of Science in Physics and Astronomy: Minor Research is composed of 30 ECTS compulsory courses, 30 ECTS master thesis, 10-12 ECTS external mobility courses and 48-50 ECTS minor Research Electives. Our Master is jointly organized with UGent.\n\nPhysics aims at understanding the world around us by observing it from the smallest scales to the scale of the universe itself. From those observations, models are built to allow us to understand, explain and eventually predict the behavior of nature. The Master in Physics and Astronomy provides a comprehensive education in physics covering the particle physics, general relativity, astrophysics and the study of complex systems.\n\nThis master will give you quantitative and analytic skills that are useful to solve many problems arising in many areas beyond physics."@en . . . "2"@en . "TRUE" . . "Master"@en . "Thesis" . "1092.10" . "Euro"@en . "3620.00" . "Recommended" . "As a physicist you will be in high demand on the job market. With a master in Physics and Astronomy from VUB, you will have the knowledge and skills to land a job in one of many diverse sectors.\n\nThere is plenty of work in scientific research at universities and research institutes. In industry, in modelling, statistics and informatics. Alternatively, work on risk analysis and modelling in the banking, finance or pharmaceuticals sectors. You will also be valuable in the field of education. Infinite opportunities, in fact!"@en . "2"^^ . "TRUE" . "Upstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .