Weekly update week 44

Dear all, here's the weekly FYS-STK3155/4155 digest.

Last week we ended our discussions on deep learning methods, with an emphasis on convolutional neural networks and recurrent neural networks. We started discussing the principal component analysis method for dimensionality reductions. We will wrap up this part this week as well as discussing briefly another dimensionality reduction method like clustering and k-means. 

We will also discuss various aspect of project 2 as well at the beginning of Thursday's lecture.

After this, we start with our second last topic for this semester (most likely on Friday), namely decision trees, random forests and other ensemble methods (bagging, voting and boosting). 

This will keep us busy till the end of next week. Thereafter we move to the very final topic, support vector machines. 

We would also like to discuss possible data sets for project 3 , possibly either Thursday November 18 or Friday the 19th. Feel free to suggest data sets you would like to explore.  Last year different groups presented data sets they wanted to work on for project 3. This functioned like a workshop during the lecture and was very successful. 

We plan also to have feedback fro project 1 ready by Monday/Tuesday next week, roughly one week before the deadline for project 2

The schedule for this week is

- Lab Wednesday: Work on project 2 (remember hat we have also digital labs 815-10 and 1415-16.
- Lecture Thursday: Discussion of project 2. Summary of PCA and discussion of Clustering for unsupervised learning. Decision trees, classification and regression
- Lecture Friday: Decision trees, basic algorithms
- Reading recommendations:
  - See lecture notes for week 44 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
  - Hastie et al sections 9.1 and 9.2. Geron's text chapter 6 (Decision trees) and chapter 8 on PCA and Clustering
 

Best wishes to you all,

Morten et al