With the rise of new technologies, the volume of omics data in the fields of biology and medicine has grown exponentially in recent times and a major issue is to mine useful predictive knowledge from these data. Machine learning (ML) is a discipline in which computer algorithms perform automated learning by using data in order to assist humans to deal with the large volume of multidimensional data. The analysis of such data is not trivial and ML is a necessary tool to extract knowledge and make predictions that can advance the field of bioinformatics.
This 2-day course will introduce participants to common ML algorithms and teach how to apply them to omics data in extensive practical sessions. The practical sessions will be conducted in Python3 based on the widely applied scikit-learn ML framework. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML methods and processes, as well as the practical skills in applying them to real world problems using publicly available biological or medical data sets.
This course is intended for PhD students, post-docs and staff scientists who are interested in applying ML to analyze these data.
At the end of the course, the participants are expected to:
No prior knowledge of ML concepts and methods is required.
Knowledge of different -omics data is recommended.
Familiarity with the Python programming language and pandas dataframes as well as a basic knowledge on statistics is required.
The competences and knowledge levels required correspond to those taught in courses such as: First Steps with Python in Life Sciences, Introduction to statistics with Python and Introduction to statistics with R. Test your skills with Python and statistics with the quiz here, before registering.
This course will be streamed, you are thus required to have your own computer with an Internet connection.
Additionally, you will need to have a recent python3 as well as a number of python libraries installed. Please follow these instructions to setup your environment (note: these instructions use conda to manage the different packages)
Please perform these installations PRIOR to the course and contact us if you have any trouble.
Finally, although not mandatory, we also highly recommend you to use the same computer to connect to the zoom classroom and perform the exercises, otherwise we will have difficulties helping you debug your code.
The registration fees for academics are 200 CHF and 1000 CHF for for-profit companies. While 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.
You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.
Deadline for registration and free-of-charge cancellation is set is set to 25/09/2023. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.
This course will take place in Zurich at the Chemistry | Biology | Pharmacy Information Center.
The course will start at 9:00 CET and end around 17:00 CET.
Precise information will be provided to the participants in due time.
Coordination: Valeria Di Cola, SIB Training Group.
We will recommend 0.50 ECTS credits for this course (given a passed exam at the end of the course).
You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.
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For more information, please contact firstname.lastname@example.org.