Introduction to Machine Learning - streamed

Date 22 - 23 July 2020
Speaker(s) Fotis E. Psomopoulos, Shakuntala Baichoo
ECTS 0.5
Fees *academic: 120 CHF   -   for-profit: 600 CHF
Cancellation deadline 8 Jul 2020
City Streamed from Greece and Mauritius
*academic fee includes non for-profit organisations as well as unemployed participants at the time of application.

We are sorry but this course is oversubscribed, with a long waiting list. Sign up here to be informed of the next course.

Overview

With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially in recent times and a major issue is to mine useful knowledge from these data which are also heterogeneous in nature. Machine learning (ML) is a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine learning can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics.

This 2-day course will introduce participants to the machine learning taxonomy and the applications of common machine learning algorithms to omics data. The course will cover the common methods being used to analyse different omics data sets by providing a practical context through the use of basic but widely used R libraries. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets.

Audience

This course is intended for master and PhD students, post-docs and staff scientists familiar with different omics data technologies who are interested in applying machine learning to analyse these data. No prior knowledge of Machine Learning concepts and methods is expected nor required.

Learning objectives

At the end of the course, the participants are expected to:

  • Understand the ML taxonomy and the commonly used machine learning algorithms for analysing “omics” data
  • Understand differences between ML algorithms categories and to which kind of problem they can be applied
  • Understand different applications of ML in different -omics studies
  • Use some basic, widely used R packages for ML
  • Interpret and visualize the results obtained from ML analyses on omics datasets
  • Apply the ML techniques to analyse their own datasets

Prerequisites

Knowledge / competencies

Familiarity with any programming language will be required (familiarity with R will be preferable).

Technical

This course will be streamed, you are thus required to have your own computer with an internet connection.

In order to ensure clear communication between Instructors and participants, we will be using collaborative tools, such as Google Drive, Dropbox or Google Colab.

Application

We are sorry but this course is oversubscribed, with a long waiting list.

Registration fees are 120 CHF for academics and 600 CHF for for-profit companies. This includes course content material.

Deadline for registration and free-of-charge cancellation is set is set to 08/07/2020. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

You will be informed by email of your registration confirmation.

Venue and Time

This course will be streamed to registered participants. More information will be sent in due time.

The course will start every day at 9:00 CET and end around 17:00 CET.

Additional information

Coordination: Monique Zahn

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 training@sib.swiss.