Date(s) - 2019-10-14
09:30 CEST - 16:30 CEST
EMBL Heidelberg, Room 202
Anna Kreshuk (EMBL Heidelberg)
About this course
This workshop on the principle of Machine Learning will take place as a classroom discussion on the topics listed below. Our learners will be expected to carry out self-study based on No longer available (digital lectures) before this workshop.
On 14 October 2019, this classroom-based workshop will provide our learners with an opportunity to ask questions related to the topics listed below and revisit a few theories that may require clarification or discussions. Programming and coding problems will NOT be discussed by the trainer. Any additional detail will be shared with the registered participants via email.
Please note that this workshop is particularly aimed at our learners who would like to prepare for the practical
mlr workshop offered on 6-7 November 2019 by EMBL Bio-IT.
- Introduction to Machine Learning
- Linear models, regression,
- Classification: Logistic regression, LDA, QDA, Naive Bayes
- Random forest
These topics are covered in the No longer available (digital lectures), in the parts for Day-1, Day-2, and Day-4.
Topics covered in the parts for Day-3 and Day-5 is not required for this workshop and will be covered in the practical
mlr workshop (register separately).
Pre-workshop digital lectures
The No longer available (digital lectures) should be as self-contained and enable self-study as much as possible. The major part of the material is provided as slide sets with lecture videos. We have also prepared interactive tutorials where you can answer multiple-choice questions.
The module offers an introductory and applied overview of “supervised” Machine Learning, i.e., regression and classification. This includes models such as linear regression, discriminant analysis, naive Bayes, decision trees and random forests, and their evaluation, with cross-validation and ROC analysis.
The course is of introductory nature and aimed at a practical and operational understanding of the covered algorithms and models, with less emphasis on theory and formalism.
This one-day workshop will provide you with an opportunity to discuss problems, ask questions, and clarify the concepts related to the topics listed above, or related theories in Machine Learning.
Please note that the maximum capacity of this course is 40 participants and registration is required to secure a place. If you have any questions, please contact Malvika Sharan.
Please note that even though this workshop will serve as a pre-requisite for the
mlr practical session in November, registrations for both the workshops will be done separately.
There is no registration fee for this workshop.
This event is fully booked.