BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260618T210411Z UID:28f1518e-8fbe-4ce9-aee7-98dcce3e10cd DTSTART:20221208T090000Z DTEND:20221209T170000Z DESCRIPTION:**This course is now full with a long waiting list.** If you d o not want to miss your chance to be part of the next session and remain i nformed about all training activities at SIB\, we highly recommend you to keep an eye on our list of [upcoming events](https://www.sib.swiss/trainin g/upcoming-training-courses) and subscribe to our courses [mailing list](h ttps://lists.sib.swiss/mailman/listinfo/courses) here (if you haven't done so already).\n\n# Overview\nStatistics are an integral aspect of scientif ic research\, and in particular of life sciences that heavily rely on quan titative methodologies. Among other things\, statistics are an essential t ool which allows gaining new insights on the relationships between differe nt biological measurements and variables. \n\nMachine learning (ML) also a ssists in making sense of large and complex datasets and can be very usefu l in mining large biological datasets to uncover new insights that can adv ance the field of bioinformatics.\n\nThis course was designed to guide par ticipants in the exploration of the concepts of statistical modelling\, an d at the same time relate and contrast them with machine learning approach es when it comes to both classification and regression.\n\nA particular fo cus will be given on the evaluation of the relevance of the produced model s\, and their interpretation in order to provide new biological knowledge. \n\n# Audience\nThis course is addressed to life scientists who want to ha ve a better understanding of these methods and on how to apply them to the ir own datasets. \n\n# Learning outcomes\nAt the end of the course\, the p articipants will be able to:\n * perform linear and logistic regressions\, and critically evaluate their results\n * describe the general Machine Le arning data analysis pipeline\n * implement a classification task and appr aise the resulting model\n * contrast the statistical and Machine Learning approaches when it comes to regression\, and choose the most appropriate to their question.\n\n\n# Prerequisites\n##### Knowledge / competencies\nT he course is targeted to life scientists who are already familiar with the Python programming language and who have basic knowledge on statistics. T he competences and knowledge levels required correspond to those taught in courses such as: [First Steps with Python in Life Sciences](https://www.s ib.swiss/training/course/20220928_PYTFS)\, [Introduction to statistics wit h Python](https://www.sib.swiss/training/course/20220620_STATP) and [Intr oduction to statistics with R](https://www.sib.swiss/training/course/20220 207_STATR).\n\n\nBefore applying to this course\, please self assess your Python and statistics skills using the quiz [here.](https://forms.gle/ZpQF yHHwoPQKJSwv7) \n\n\n\n##### Technical\nYou are required to have your own computer with an internet connection and the following tools installed PRI OR to the course:\n* one of the latest Python 3 distributions (preferably above or equal to python 3.8)\, for instance installed with [conda](https: //docs.continuum.io/anaconda/install/) \n* [Jupyter](https://jupyter.org/i nstall)\n* the [scipy](https://www.scipy.org/install.html) library (NB: if you installed conda\, then this library is already installed)\n* [scikitL earn](https://scikit-learn.org/stable/install.html) \n* [statsmodels](http s://www.statsmodels.org/stable/install.html) \n\n\n\n# Schedule \n\nDay 1 \n* Warm-up: loading and plotting data with python. \n* Linear modelling: ordinary least squares\, from fitting to models comparison\n* Logistic reg ression and Generalized Linear Models (GLM): from regression to classifica tion\n\nDay 2 \n* The Machine Learning pipeline and evaluation\n* Machine Learning and classification: logistic regression classifier and random fo rests\n* Machine Learning and regression\n\n# Application\n\nThe course is now full with a long waiting list.\n\nThe registration fees for academics are 120 CHF and 600 CHF for for-profit companies.\n\nYou will be informed by email of your registration confirmation. Upon reception of the confirm ation email\, participants will be asked to confirm attendance by paying t he fees within 5 days.\n\nApplications will close as soon as the maximum c apacity is reached. Deadline for free-of-charge cancellation is set to **2 4/11/2022**. Cancellation after this date will not be reimbursed. Please n ote that participation in SIB courses is subject to our [general condition s](https://www.sib.swiss/training/terms-and-conditions).\n\n# Venue and Ti me\nThis course will be streamed from Basel.\n\nThe course will start at 9 :00 and end around 17:00 (CET time zone). \n\nPrecise information will be provided to the participants in due time.\n\n\n# Additional information\n Coordination: Valeria Di Cola\, SIB training group\n\nWe will recommend 0. 5 ECTS credits for this course (given a passed exam at the end of the cour se).\n\nYou are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops\, as well as all important de adlines using the form [here](https://lists.sib.swiss/mailman/listinfo/cou rses).\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://elixir-europe.org/e vents/code-of-conduct). Participants of SIB courses are also required to a bide by the same code.\n\nFor more information\, please contact [training@ sib.swiss](mailto://training@sib.swiss). SUMMARY:Statistics and Machine Learning for Life Sciences URL;VALUE=URI:https://www.sib.swiss/training/course/20221208_STAML END:VEVENT END:VCALENDAR