This lesson presents basic machine learning concepts as presented in Data Analysis for the Life Sciences by Rafael A. Irizarry and Michael I. Love.
Prerequisites
To succeed in this course, you should:
have basic R programming skills
have basic statistics knowledge and skills.
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction |
What is machine learning?
How is it used in biomedical studies? |
| 00:10 | 2. Clustering | How clusters within high dimensional data can be discovered? |
| 01:10 | 3. Conditional Probabilities and Expectations | How to get statstical information from specific subsets in our data? |
| 02:10 | 4. Smoothing | Can a model be fitted to a dataset which shape is unknown but smooth? |
| 03:10 | 5. Class Prediction |
What is machine learning (ML)?
Why should we learn ML? |
| 04:10 | 6. Cross-validation | How can the best configuration of parameters be selected for a machine learning model using only the data available? |
| 05:10 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.