# Meeting #2 (02/12/2021)—Individual Discussion
11:00-13:00, Room 9001/B54
## Tridham
- Went over the reading and notes collected so far with regards to the project
- Discussed that variable selection may be an alternative to dimensionality reduction to reduce size of dataset
- Was given list of areas to read futher from ISLR including : Variable selection, Quadratic/Linear Discriminant analysis , Logisitc Regression, Naive Bayes, K - Nearest Neighbours, Regression / Classification trees , SVM
- Next steps include : Getting familiar with dataset , searching for more academic sources, thinking about missing values, trying to see how other people have achieved music recommendation algos and attempting data pre-processing and summary information

## Yuhao
- A brief study of An Introduction to Statistical Learning
Learning, and a video course on Machine Learning
- Initial identification of the topic and search for corresponding data and information
- Write an outline of the research objectives, screen the machine learning methods and match the required content to the outline

## Tabitha
- Went over possible options of a project looking at PCA
- Briefly looked through references
- Discussed format and details of report
- Next steps are to read through references and literature and look at a few topics more closely, including the drawbacks of PCA, robust PCA etc.

## Quentin
- Discussed prior reading, confirmed that chosen dataset would be appropriate
- Recieved suggestions for which ML techniques to research: Variable selection, Quadratic/Linear Discriminant analysis , Logisitc Regression, Naive Bayes, K - Nearest Neighbours, Regression / Classification trees , Support Vector Machine
- Given outline of report format and presentation in january
- Next Steps: Investigate dataset further, decide on a programming language to complete project, research suggested ML tools, decide on title for the report.