Academic English in Statistics (2016 Fall)

General Information

Part I: General Topics

  1. The way we read and present academic work
  2. A brief language guide for statistical writing
  3. The structure of statistical papers
  4. Typesetting your manuscript
  5. Academic Misconduct Cases

Part II: Statistical Topics

Guide for presentations

You are supposed to work as a team (2-4 people) for each presentation topic. Please reply below to select the topic (with “Presentation Topic”) you want to present. This work counts 50% of your final score.

Presentation Topics

  1. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 (2015): 436-444. GROUP A.B.C.D
  2. Erhan, Dumitru, et al. “Why does unsupervised pre-training help deep learning?.” Journal of Machine Learning Research 11.Feb (2010): 625-660.
  3. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” In Advances in neural information processing systems, pp. 1097-1105. 2012.
  4. Rönnqvist, Samuel, and Peter Sarlin. “Bank distress in the news: Describing events through deep learning.arXiv preprint arXiv:1603.05670 (2016).
  5. Lectures:
    1. Introduction to Bayesian Inference
    2. Fundamentals of Bayesian Data Analysis
    3. Introduction to MCMC methods

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