An introduction to machine learning in R.
Prerequisites
A basic understanding of R. Material covered at the beginning of the week cover sufficient background.
An introduction to machine learning in R.
Prerequisites
A basic understanding of R. Material covered at the beginning of the week cover sufficient background.
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction to machine learning | What is machine learning? |
| 00:40 | 2. Clustering | How can we use clustering to find data points with similar attributes? |
| 01:15 | 3. Dimensional Reduction | How can we perform unsupervised learning with dimensionality reduction techniques such as Principle Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE)? |
| 01:15 | 4. Regression |
How can I make linear regression models from data?
How can I use logarithmic regression to work with non-linear data? |
| 02:30 | 5. day 1 practical | |
| 02:40 | 6. Non-Linear Classifiers | How can I process data? |
| 03:15 | 7. Neural Networks | How can we classify images using a neural network? |
| 04:05 | 8. Ethics and Implications of Machine Learning | What are the ethical implications of using machine learning in research? |
| 04:20 | 9. Find out more | Where can you find out more about machine learning? |
| 04:30 | 10. day 2 practical | |
| 04:40 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.