Languages and machine learning are combined in the NLP course, which emphasizes models, translation, and applications of NLP to various fields. I am interested in training machine translation models and 'peeking inside' their architecture to learn languages better myself. In other words, if I understand how machines translate, and what rules/correlations they use, I can use those same rules to learn languages more efficiently.

The class problem sets were very difficult, with full-length challenges on building a translation model from sentence and data pre-processing to evaluating results. The 'software experience' of e.g. debugging and planning was embedded (mind the pun) in this class. Near the end, a series of guest lectures brought NLP to human interpretation of languages, healthcare record streamlining, and financial report analysis. The final project was the ultimate highlight of NLP; feel free to view my group's work here!