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Schedule

Setup Download files required for the lesson
00:00 1. Welcome to this workshop What is the purpose of this training?
What are the learning goals and objectives?
What will this workshop not cover?
What next steps should be taken after this course?
00:10 2. Data Science, AI, and Machine Learning What is Data Science and Artificial Intelligence?
What is Machine Learning and how do they apply in biomedical research?
What are some relevant examples of Deep Learning and Large Language Models?
00:43 3. AI for Automation How is AI used for automating tasks in biomedical experimental setups?
What are examples of biomedical AI-driven software packages and what can they be used for?
00:55 4. AI for Data Insights How is AI used for data insights in biomedical experimental setups?
What types of data insights can be generated?
00:55 5. Problems with AI What are the common pitfalls of using machine learning?
What are common limitations and pitfalls in ML applications?
What are conscious and unconscious biases that might influence ML algorithms?
How can data privacy and data security be ensured?
Who is responsible and accountable for any ethical issues implied by ML utilisation?
00:55 6. Practical Considerations for Researchers What are the necessary steps before research data can be processed through ML pipelines?
What types of data cleaning can be applied to prepare raw data for ML?
01:45 7. Practical Considerations: Reporting Results How do research results differ with regards to Supervised versus Unsupervised Learning?
What are best practices for responsible reporting results from ML pipelines?
02:35 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.