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Machine Learning in R (advanced course)

Machine Learning in R (advanced course)

Start Date:
Jun 12, 2018
Finish date:
Jun 13, 2018
Booking deadline:
Jun 5, 2018
Last Revised:
Jun 27, 2018
Code:
2018-mlr_embl
Hits:
915
Favoured:
0 Favoure
Price:
free
Location:
Not Specified
Group:
Not Specified
Level:
Advanced
Available seats:
-6 of 30

Description

** This course is full. If you want to be added to the waiting list, please email malvika.sharan@embl.de with a short motivation statement for this course. Preference will be given to the participants who may use machine learning in their current work. **

Dates: 12/06/2018 - 13/06/2018

Time: 09:30 - 17:30

Location:

EMBL Heidelberg, Room: 202

Tutors and helpers

- Prof Bernd Bischl, Xudong Sun (Ludwig-Maximilians-University Munich)
- Michel Lang (TU Dortmund)
- Georg Zeller, Bernd Klaus (EMBL Heidelberg)

Organised by the Bio-IT Project

Course Information

This two-day course, delivered by experts in programming for data analysis, will teach participants the principle of machine learning and its implementation in R using mlr package. The main goal of mlr is to provide a unified interface for machine learning tasks as classification, regression, cluster analysis and survival analysis in R.

Sessions will be driven by many practical exercises and case studies. The schedule and course materials will be added here.

On June 11, 2018, 6:00 PM at EMBL Staff Lounge, an informal seminar event will be hosted (open for attendance by non-course-attendees also) where Prof. Bernd Bischl will give a short talk on OpenML project, followed by Beer and Pizza session. **Title TBA**.

Course Content

This 2-day course will cover the following topics and sessions:

Sessions on Day-1
1. Introduction to Machine Learning, mlr, KNN, and its _application in a biological dataset_
2. Linear models, regression, regularization, and trees
3. Evaluation (train, test, ROC), and its _application to microbiome-based cancer detection_
4. Hands-on session: application on a new dataset (see the prerequisite #3)

Sessions on Day-2
5. Forests and boosting with a demo
6. Tuning and nested resampling with a demo
7. Interpretable machine learning and feature selection
8. Hands-on session: application on the dataset from Session-4

Prerequisites

  1. The course is aimed at participants preferably with some knowledge of statistics and data modeling, and want to learn more about machine learning and its application and implementation through the hands-on sessions and use cases. The participants are expected to understand the concepts described in these materials before the workshop.
  2. Participants are expected to bring their own laptop with R version >=3.3.2 installed.
  3. Please create a Kaggle account for the hands-on sessions.

Optional: The participants can have a look at the mlr tutorial to gain a little head-start, but this will be covered in the lectures.

Registration

**This course is booked out.**

Please note that the maximum capacity for the course is 30 participants and registration is required to secure a place. If you have any questions, please contact Malvika Sharan and Bernd Klaus.

Course Fee:

This course will be offered for free to all EMBL members.

The external participants will be charged with a course fee of 100 Euro. The invoice details will be shared via email.

Cancellation and No-Show:

The registration can be canceled for the free of charge until June 2, 2018.

The participants will be charged a cancellation fee (if canceled after June 2, 2018) or no-show fee of 50 Euros. The invoice details will be shared via email.


Category

All Courses