BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260716T022330Z UID:4789f730-3a4d-4834-8cb2-e61dba09ba86 DTSTART:20261117T090000Z DTEND:20261117T170000Z DESCRIPTION:## Overview \n\nMachine Learning has become an essential tool i n Life Science\, letting scientists explore and learn from large and compl ex biological datasets. To collectively unravel the puzzle of life\, we mu st ensure that machine learning models make the most of the available data and that they are correctly generalizable\, robust\, and interpretable to provide trustworthy and actionable insights. This advanced course is desi gned for scientists who already have a foundational understanding of machi ne learning and seek to enhance their core skills in this domain. \n\nTh is course focuses on best practices and advanced techniques in Machine Lea rning\, aiming to provide you with the tools needed to develop more accura te\, generalizable\, and transparent models. \n\n\n## Audience \n\nThis course is designed for PhD students\, postdoctoral and other researchers in the life sciences from both academia and industry whowho would like to learn more about general best practices in Machine Learning and get more o ut of their Machine Learning models: more precise hyper-parameters\, more generalizable models\, and more interpretable models. \n\n\n## Learning ou tcomes \n\nAt the end of this course\, you will be able to:\n\n* **Use** the hyperopt library to efficiently explore your hyper-parameter space wit h Bayesian Optimization and tune your models. \n* **Evaluate** the genera lizability of your generated models using best practices such as nested cr oss-validation. \n* **Explain** the role of each feature in your model's prediction\, even for so-called "black-box" models \n* **Examine** the r esults of your models and assess their quality. \n\n## Prerequisites \n\n ##### Knowledge / competencies \n\n* Good knowledge of the basics of mach ine learning\, such as K-fold cross-validation\, Decision Tree\, and evalu ation metrics. \n\n* Fluency with the Python programming language\, inclu ding working knowledge of standard data analysis libraries such as numpy\, pandas\, matplotlib\, and scikit-learn. \n\n* Familiarity with different omics data technologies (highly recommended). \n\nThis course is part of the [Machine Learning](https://www.sib.swiss/training/learning-paths?path =machine-learning) learning path. To get the most out of this course\, you should meet the learning outcomes of [Introduction to Machine Learning wi th Python](https://www.sib.swiss/training/course/20261001_INMLP) and [Stat istics and Machine Learning for Life Sciences](https://www.sib.swiss/train ing/course/20260331_STAML).\n\n\nBefore applying to this course\, please s elf-assess your Python and Machine Learning skills using the [**quiz**](ht tps://forms.office.com/e/aWAGV8tbix) here. We recommend a score of at le ast 6/10. \n\n##### Technical \n\nYour laptop must have a recent Pytho n version (minimum 3.0) and several Python libraries installed. The needed libraries will be indicated in the course [GitHub repo](https://github.co m/sib-swiss/intermediate-machine-learning-training/) and here in due time. \n\n## Schedule - CET time zone \n\n| Schedule CEST | | \n| --- | ---- ------- |\n| 09:00-12:00 | Theory\, demonstration and micro-exercises | \ n | 12:00-13:00 | Lunch break | \n | 13:00-16:00 | Group work | \n | 16:00-17:00 | Group work debrief and conclusions | \n\nPlease take note of the following information: \n\nThis schedule is subject to change to allo w time for questions and discussions with the course participants. \n\nThe group work involves a project designed by the course trainers\, where par ticipants collaborate in smaller groups to address the same question. \n\n In addition to the suggested projects\, the group work may also incorporat e some Bring Your Own Data components. However\, this depends on the data' s cleanliness\, the feasibility of your objective\, and the interest of ot her participants. In any case\, the instructors will be happy to discuss y our data. \n\n## Application \n\nThe registration fees for academics are 1 00 CHF and 500 CHF for for-profit companies. \n\nYou will be informed by e mail of your registration confirmation. Upon reception of the confirmation email\, participants will be asked to confirm attendance by paying the fe es within 5 days. \n\nWhile participants are registered on a first come\, first served basis\, exceptions may be made to ensure diversity and equity \, which may increase the time before your registration is confirmed. \n\n Applications close on **03.11.2026** or as soon as the maximum capacity ha s been reached. The deadline for free-of-charge cancellation is set to **0 3.11.2026** . Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our [general conditio ns](https://www.sib.swiss/training/terms-and-conditions). \n\n## Venue and Time \n\nThis course will be streamed. \n\nThe course will start at 9:00 and end around 17:00. Precise information will be provided to the partici pants in due time. \n\nPrecise information will be provided to the partici pants in due time. \n\n## Additional information \n\nCoordination: Grégoi re Rossier\, SIB Training Group.\n\nAt the end of the course\, we will pro vide a *Certificate of Attendance* or a *Certificate of Achievement* recom mending 0.25 ECTS credits (given a passed exam). \n\nYou are welcome to re gister to the SIB courses mailing list to be informed of all future course s and workshops\, as well as all important deadlines using the form [here] (https://lists.sib.swiss/postorius/lists/courses.lists.sib.swiss/). \n\nPl ease note that participation in SIB courses is subject to our [general con ditions](https://www.sib.swiss/training/terms-and-conditions). \n\nSIB abi des by the [ELIXIR Code of Conduct](https://elixir-europe.org/events/code- of-conduct). Participants of SIB courses are also required to abide by the same code. \n\nFor more information\, please contact [training@sib.swiss] (mailto://training@sib.swiss). SUMMARY:Ensuring More Accurate\, Generalisable\, and Interpretable Machine Learning Models for Bioinformatics URL;VALUE=URI:https://www.sib.swiss/training/course/20261117__INTML END:VEVENT END:VCALENDAR