BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260407T175105Z UID:6b60136d-6ef1-48d1-9a14-a83029904abb 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 addressed to life scientists\, bioinformaticians\, and computat ional biologists who would like to learn more about general best practices in Machine Learning and get more out of their Machine Learning models: mo re precise hyper-parameters\, more generalizable models\, and more interpr etable models. \n\n\n## Learning outcomes \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 with Bayesian Optimization and tune your model s. \n* Evaluate the generalizability of your generated models using best practices such as nested cross-validation. \n* Explain the role of each feature in your model's prediction\, even for so-called "black-box" models \n* Examine the results of your models and assess their quality. \n\n## Prerequisites \n\n##### Knowledge / competencies \n\n* Good knowledge of the basics of machine learning\, such as K-fold cross-validation\, Decisi on Tree\, and evaluation metrics. \n\n* Fluency with the Python programmi ng language\, including working knowledge of standard data analysis librar ies such as numpy\, pandas\, matplotlib\, and scikit-learn. \n\n* Familia rity with different omics data technologies (highly recommended). \n\nThe competencies and knowledge levels required correspond to those taught in courses such as [Introduction to Machine Learning in Life Sciences](https: //github.com/sib-swiss/intro-machine-learning-training). \n\nBefore applyi ng to this course\, please self-assess your Python and Machine Learning sk ills using the [**quiz**](https://forms.office.com/e/aWAGV8tbix) here. W e recommend a score of at least 6/10. \n\n##### Technical \n\nYour lap top must have a recent Python version (minimum 3.0) and several Python lib raries installed. The needed libraries will be indicated in the course [Gi tHub repo](https://github.com/sib-swiss/intermediate-machine-learning-trai ning/) and here in due time. \n\n## Schedule - CET time zone \n\n| Sched ule CEST | | \n| --- | ----------- |\n| 09:00-12:00 | Theory\, demonstra tion 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 conclusio ns | \n\nPlease take note of the following information: \n\nThis schedule is subject to change to allow time for questions and discussions with the course participants. \n\nThe group work involves a project designed by the course trainers\, where participants collaborate in smaller groups to add ress the same question. \n\nIn addition to the suggested projects\, the gr oup work may also incorporate some Bring Your Own Data components. However \, this depends on the data's cleanliness\, the feasibility of your object ive\, and the interest of other participants. In any case\, the instructor s will be happy to discuss your data. \n\n## Application \n\nThe registrat ion fees for academics are 100 CHF and 500 CHF for for-profit companies. \ n\nYou will be informed by email of your registration confirmation. Upon r eception of the confirmation email\, participants will be asked to confirm attendance by paying the fees within 5 days. \n\nWhile participants are r egistered on a first come\, first served basis\, exceptions may be made to ensure diversity and equity\, which may increase the time before your reg istration is confirmed. \n\nApplications close on **03.11.2026** or as soo n as the maximum capacity has been reached. The deadline for free-of-charg e cancellation is set to **03.11.2026** . Cancellation after this date wil l not be reimbursed. Please note that participation in SIB courses is subj ect to our [general conditions](https://www.sib.swiss/training/terms-and-c onditions). \n\n## Venue and Time \n\nThis course will be streamed. \n\nT he course will start at 9:00 and end around 17:00. Precise information wil l be provided to the participants in due time. \n\nPrecise information wil l be provided to the participants in due time. \n\n## Additional informati on \n\nCoordination: Grégoire Rossier\, SIB Training Group.\n\nAt the end of the course\, we will provide a *Certificate of Attendance* or a *Certi ficate of Achievement* recommending 0.25 ECTS credits (given a passed exam ). \n\nYou are welcome to register to the SIB courses mailing list to be i nformed of all future courses and workshops\, as well as all important dea dlines using the form [here](https://lists.sib.swiss/postorius/lists/cours es.lists.sib.swiss/). \n\nPlease note that participation in SIB courses is subject to our [general conditions](https://www.sib.swiss/training/terms- and-conditions). \n\nSIB abides by the [ELIXIR Code of Conduct](https://el ixir-europe.org/events/code-of-conduct). Participants of SIB courses are a lso 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