We will continue our discussion of optimization see - https://www.overleaf.com/read/zppqchcctnwf appendix A.1

Also added two exercises to the appendix. 

Guy presented the solution to exercise 2.3.1.   You can view the exercise here https://www.overleaf.com/read/zppqchcctnwf   Clicking on the exercise will get you to a notebook where you can run the solution.   

 

"Why do we need the Lebesgue integral in probability" meeting recording - https://www.youtube.com/watch?v=WbfBEbg6aXE&list=PLC7m9qp0Q1Ye3Fwnooz2k5xh0p5eD1s4k&index=45

 As part of the optimization thread.    Meeting recording on when a function is bounded - https://www.youtube.com/watch?v=q5r-8x-ST3g&list=PLC7m9qp0Q1Ye3Fwnooz2k5xh0p5eD1s4k&index=44

 A nice resource on optimization with a focus on convex optimization - 

https://m.youtube.com/playlist?list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc

 Back to optimization.  We will lay down some of the foundations and discuss when a function is bounded https://drive.google.com/file/d/15gz6KizutYMlzTheTE8HymFHtDar7jHE/view?usp=sharing

 

Today Samuel Ackerman spoke on "statistically determining which model to choose by comparing performance over cross-validation runs".  Details are on his blog  - https://www.research.ibm.com/haifa/dept/vst/ML-QA.shtml