Machine Learning and Natural Language

Spring 2016

Course Plan and Lecture Notes

Note: Topics, Lecture Notes, Relevant Papers and Presentations will be made available and will be updated throughout the semester.
Papers that are recommended for presentation are denoted by P along with their category

        I.    Introduction

  1. Introduction to the Class [PPT] [PDF] (01/20)
    1. NLP Problems; Key Approaches

  2. Models of Classification and Multiclass Classification (01/27)
  3. Reading:

        II.    Basic Structured Models: Sequential Models

  4. Sequence Labeling Problems (2/3, 2/10)

  5. (02/18, 02/23, 02/25)
    1. HMMs and CRFs
    2. Inference with Classifiers I
    3. Structured Perceptron
    4. Structured SVMs

    Reading:

        III.    Constrained Conditional Models

    1. Pipeline Models
    2. Integer Linear Programming
    3. Introducing Background knowledge

    Reading:

        IV.    Training Paradigms

    1. Decoupling Learning from Inference (L+I)
    2. Inference based Training (Joint Learning, IBT)
    3. Online and Batch Joint Learning

    Reading:

        V.    Unsupervised Learning and Indirect Supervision

    1. Constraints Driven Learning and Posterior Regularization
    2. Learning with latent variables
    3. Indirect Supervision

    Reading:

        VI.    Inference

    1. Approximate Inference
    2. Dual Decomposition

    Reading:

    Group Presentations Schedule (Tentative: order of the groups is pretty firm)

    Tentative Presentation Schedule (google doc)