11-745 Advanced Statistical Learning Seminar
6 units, Carnegie Mellon University
This seminar aims to deepen the participants understanding of the theoretical foundation of statistical learning and applications, to broaden their knowledge of new techniques, directions and challenges in the field, and to inspire research ideas through classroom discussions. In the past years, this seminar was structured as a 12unit course in the form of groupreading, presenting and discussing the book chapter by chapter of The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie et al. In the fall of 2014, we will have a 6unit seminar, focusing on New Methods in Largescale Structured Learning, with selected papers from the past 23 years of ICML, NIPS, KDD, JMLR or the like, plus related background reading. We will meet once a week, one topic per week 12 papers, with presentations rotating among participants. In each week, the assigned presenter starts with the questions from all the participants collected by email about the current topic, followed by a presentation on that topic and leads the discussion. All the students are required to read the assigned papers of the week before the class, and email their questions to the presenter CC to everybody. By the end of the semester, each student will individually write a short 34page white paper outlining a research proposal for new work extending one of the research areas discussed in class. There will be no exams or homework the grading is based on class participation, quality of the seminar presentations, questions submitted for each class and discussions, and the final brief paper. Prerequisites Intro machine learning and related courses
Prerequisites: none listed
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