Short Description

Advanced machine learning methods, with an undercurrent of natural language processing (NLP) applications. Bag of words, recommender systems, topic models, ranking, natural language as sequence data, POS tagging, CRFs for named entity recognition and RNNs for text synthesis. An introduction to popular NLP libraries in Python.

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Compare and contrast classifiers that generate binary predictions and those that compute probabilistic predictions; derive the probabilistic predictions of the Naive Bayes method.
  2. Apply graphical models as a probabilistic approach to model complex, large-scale problems.
  3. Apply basic techniques in active data acquisition, and explain under what circumstances these techniques are worth using.
  4. Make use of pairwise preference data via ranking algorithms
  5. Identify when recommender systems may be useful and apply them in these circumstances.

Instructor (2016-2017)

Note: information on this page is preliminary and subject to change.