Semester page for FYS-STK4155 - Autumn 2019

Dear all, we hope all is well with final exams and project 3. We have been made aware of the fact that many of you this year have exams close to our deadline for project 3, December 15 (there was an error in devilry btw). 

We decided thus to move the final deadline to December 18 at the devilrish time 2359 (1159pm), hoping this can be of help. That is the final deadline.  It will be updated soon in devilry.

If you have not received feedback on project 1 let us know. Feedback on P2 is being added as of now and by the end of this week it should be there. It has taken us more time than expected to finalize the feedback. With 130 participants and fantastic projects we have clearly underestimated the time it takes to wrap this up. Also, by our bylaws on grading, every project has to be graded by two of us. We apologize for the delay in obtaining the final feedback. Anyway, the quality on all projects is very high and...

Dear All, this is or last week of organized teaching activities. Today is also our last lab session this semester and our last lecture is Thursday (no lecture on Friday).

We will during Thursday's lecture summarize what we have covered this semester with an eye on possible research projects in machine learning, advanced courses and other topics of interest. We will also point to other topics such as Bayesian statistics and Bayesian machine learning as a teaser for future research directions.

In the meantime, best wishes to you all and thx so much for all heroic efforts this semester. It has been a great pleasure to get to know you all,

Hanna, Lucas, Morten, Stian and ?yvind

Dear all,

here's as promised the link to John Aiken's lecture on XGBoots. I hope you may find it interesting, and again thanks to all of you who presented possible data sets last Friday, cool.

John's slides are at https://docs.google.com/presentation/d/1A5tMrZSa7XBwSZMEFDkkfmEBlDnM1y_H_DEhu5eWJDU/edit#slide=id.p

Else, this coming Thursday we will finalize our material on Support Vector Machines, both for classification and regression, with the final equations and code examples. 

Hastie et al's chapters 4.5 and 12.1-12.4 cover much of what we plan to discuss and have discussed. The slides at ...