FIXME: home page introduction
Prerequisites
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FIXME: home page introduction
Prerequisites
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| Setup | Download files required for the lesson | |
| 09:00 | 1. What is ML: A motivating Example | How can Machine Learning be useful? |
| 09:45 | 2. What is ML: Machine Learning vs Traditional Modelling |
What is machine learning? Isn’t that just statistics?
Does machine learning even help a scientist who is interest in understanding how the universe works? |
| 10:15 | 3. Coffee Break | Break |
| 10:30 | 4. What is ML: A Taxonomy of Machine Learning |
What is the difference between classification and regression?
What is the difference between supervised and unsupervised learning? How can I quantify machine learning algorithm performance? |
| 11:00 | 5. What is ML: Why Use Machine Learning? |
What is machine learning good for?
How can it help me with scientific discovery? |
| 11:30 | 6. What is ML: When to use it |
How much data do I need to train an accurate machine learning model?
What sort of compute is required to perform machine learning? When do I want to use machine learning? |
| 12:00 | 7. What is ML: How to use it |
How do I do machine learning?
What software is available for machine learning? What does a typical machine learning project workflow look like? |
| 12:30 | 8. Data Pipelines: Getting and Viewing Data | Key question (FIXME) |
| 13:30 | 9. Lunch Break | Break |
| 14:30 | 10. Data Pipelines: Data Munging | Key question (FIXME) |
| 15:30 | 11. Data Pipelines: Data | Key question (FIXME) |
| 16:30 | 12. Data Pipelines: Tidying Data | Key question (FIXME) |
| 17:30 | 13. Data Pipelines: Feature Engineering | Key question (FIXME) |
| 18:30 | 14. Data Pipelines: Data Augmentation | Key question (FIXME) |
| 19:30 | 15. Data Pipelines: A Data Pipeline | Key question (FIXME) |
| 20:30 | 16. Data Pipelines: Recipe | Key question (FIXME) |
| 21:30 | 17. ML Models: Types of ML Algorithms | Key question (FIXME) |
| 22:30 | 18. ML Models: Types of ML Problems | Key question (FIXME) |
| 23:30 | 19. ML Models: Looking Inside the Black Box | Key question (FIXME) |
| 24:30 | 20. ML Models: Training and Learning | Key question (FIXME) |
| 25:30 | 21. ML Models: Recipe | Key question (FIXME) |
| 26:30 | 22. Testing and Verification: Introduction | Key question (FIXME) |
| 27:30 | 23. Testing and Verification: Metrics of Performance | Key question (FIXME) |
| 28:30 | 24. Testing and Verification: Overfitting | Key question (FIXME) |
| 29:30 | 25. Testing and Verification: Validation and Hyperparameter Tuning | Key question (FIXME) |
| 30:30 | 26. Testing and Verification: Recipe | Key question (FIXME) |
| 31:30 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.