Midterm topics
==============

Basic machine learning
----------------------

-  Optimization: GD, SGD, convex functions
-  MLE
-  Overfitting and underfitting
-  F1 score
-  Train/dev/test split

Text representation
-------------------

-  BoW representation
-  Feature templates
-  Word embeddings: skip-gram, CBOW

Language models
---------------

-  N-gram LM
-  Smoothing
-  Held-out likelihood, perplexity

Models
------

-  Logistic regression
-  Naive Bayes models
-  Conditional random fields
-  HMM (fully observered case)
-  Feed-forward neural nets
-  Recurrent neural nets

Inference
---------

-  Viterbi decoding