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"# Take-Home Messages\n",
"\n",
"\n",
"\n",
"* Ultimate goal of statistics is **data analysis**. E.g., PageRank, handwritten digit recognization, Netflix matrix completion, ... \n",
"\n",
" > Statistics is partly empirical and partly mathematical. It is now almost **entirely** computational. \n",
" > Kenneth Lange\n",
"\n",
"| Statisticians used to .. | Now we spend all time ... |\n",
"|----------------------------------|------------------------------------|\n",
"| | |\n",
"\n",
"\n",
"* Two essential skills for modern statisticians: **programming** and computational **algorithms**.\n",
"\n",
" > Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. \n",
" > Josh Willis on Twitter\n",
"\n",
"* **Numerical linear algebra**\n",
" * building blocks of most computing we do. \n",
" * Use standard (and good) libraries (BLAS, LAPACK, ...) as much as possible! \n",
" * Sparse linear algebra and iterative solvers such as conjugate gradient (CG) methods are critical for exploiting structure in big data.\n",
" \n",
"* **Optimization**\n",
"\n",
" * Convex programming (LS, LP, QP, GP, SOCP, SDP). Download and study Stephen Boyd's book, watch lecture vides or take UCLA EE236B by Vandenberghe, familiarize yourself with the **good** optimization softwares. Convex programming is becoming a **technology**, just like least squares (LS). Browse the documentation of `cvx` and `Convex.jl` to see which functions are implemented.\n",
"\n",
" * Generic nonlinear optimization tools: Newton, Gauss-Newton, quasi-Newton, (nonlinear) conjugate gradient, ...\n",
"\n",
" * Optimization tools developed by statisticians: Fisher scoring, EM, MM, ... Take UCLA Biomath 210 by Kenneth Lange for a thorough study of MM algorithms.\n",
"\n",
"\n",
"\n",
"* Enjoy (or hate) Julia? \n",
"JuliaCon 2018 at University College London: http://juliacon.org/2018/"
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