Robin Mesch, CME Group, Building a Trading Strategy with Market Profile, here.
Sanjeev Arora, Princeton, Advanced Topics in CS: Is Learning Easy? here.
Machine learning is a flourishing field with a plethora of algorithmic techniques. In almost all cases, we have no provable guarantees on the performance of these algorithms/heuristics —neither on their running time, nor on the quality of solutions they return. In fact in many settings, the underlying algorithmic problems are provably intractable (e.g., NP-hard or worse)! This course is an attempt to bring more rigor to machine learning. Given known intractability results, theoretical progress and theorems will probably come about from a variety of new tacks: subtle reformulation of the problem, average case approaches, approaches in between worst-case and average case, etc. We will study some early results of this kind that have been proven, and identify other open problems. The last few classes will be devoted to formulating open problems and thinking about them.
Of course, machine learning has large overlap with mathematical fields such as statistics and linear algebra, and we will bring the same viewpoint to problems in those fields.
Bloomberg, Economic Calendar, here.
Forex Factory, FX Calendar, here.
Federal Reserve Bank of NY, Tentative Outright Treasury Operation Schedule, here.