依照 John Langford 的介紹：
This tutorial focuses on providing an integrated overview of state-of-the-art platforms and algorithm choices. These span a range of hardware options (from FPGAs and GPUs to multi-core systems and commodity clusters), programming frameworks (including CUDA, MPI, MapReduce, and DryadLINQ), and learning settings (e.g., semi-supervised and online learning). The tutorial is example-driven, covering a number of popular algorithms (e.g., boosted trees, spectral clustering, belief propagation) and diverse applications (e.g., speech recognition and object recognition in vision).
不在現場的我，是沒有機會親聆這場盛會啦，不過稍微瞄了下簡報檔，覺得這本書裡面的題材都蠻有意思的，比如說下面第一個圖提到克服隱私權疑慮的嘗試，和第二個圖 Tree Ensembles 的說明。