Digital Machine Learning Course on 26 & 27 November 2020
The DHLab is organizing an online/synchronous course Machine Learning with R. Learn to build predictive algorithms with R for social sciences and humanities (postponed and gone digital due to COVID).
The course, lead by Dr. Dirk Wulff, will take place in *digital format,* on 26/27 November 2020.
The two day-program will comprise synchronous and asynchronous work units, between 9 AM-6 PM.
Please contact Irène Kälin for application (see e-mail address below).
Machine Learning with R. Learn to build predictive algorithms with R for social sciences and humanities
Machine learning is the basis for groundbreaking developments and technologies in business and many branches of research, including social sciences and humanities. Would you like to know what makes machine learning so successful and how you can reap its benefits using the open programming language R?
In this an intensive course, you will learn the fundamental principles of machine learning and how to apply them using R: from reading-in data, to the application of various algorithms and their evaluation based on key performance measures. You will learn about the characteristics of popular algorithms such as regression, decision trees, and random forests. You will hear about recent developments, such as Deep Learning, and gain an overview over typical machine learning problems, such as regression, classification, and clustering, and discuss with us the risks and benefits of machine learning for society and business.
This course takes place from 9am to 6pm on two course days.
Each day will contain a series of short lectures and examples to introduce you to new topics. The bulk of each day will be dedicated to hands-on exercises to help you ‘learn by doing’.
All course materials, tutorials, examples, exercises, and solutions will be available online for you to view at any time during and after the course.
Find the instructor’s past materials under the „past materials" button on his webpage below.
The workshop provides the essential tools to start machine learning in R.
A brief overview of the topics addressed in the course:
1. Basics of R and machine learning
2. What is fitting?
3. What is prediction?
4. What is tuning?
5. Unsupervised learning
6. Why features are key?
7. A map of algorithms
8. Looking ahead
Methods / Tools
Participants are requested to use their own laptop with software installation rights. Basic knowledge of statistics is helpful, but not strictly necessary. Detailed instructions concerning software installation and voluntary preparation is going to be provided prior to the course.
Doctoral Candidates & Postdocs. Participants should possess basic knowledge of the R language. To ensure productive learning, participants are asked to brush up on their R skills either in self-study (materials will be provided after registration).
If you are interested in participating, but you are unsure whether you meet the prerequisites for this course get in contact with the trainer or participate in the Quiz (in German) - see link below).
About the Trainer Dr. Dirk Wulff is a Swiss-based data scientist with ample experience in data science with R for both academic research and industry. His passion is to help you learn the skills needed to discover and communicate insights from data and join the data revolution.
Organisation: Dr. Berenike Herrmann, Senior Research and Teaching Associate, chair Professor Lauer (Digital Humanities Lab, University of Basel)
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