NLU-11: Week 1 | Open Course Materials

The NLU+ course consists of three live lectures a week which are also live streamed and recorded for those who are unable to attend in person. To view lectures go to Learn Ultra and click on the Lecture Recordings link.

In all cases the lecture content (excluding guest lectures which are not examinable) and the required reading are examinable, the background reading is not.

Slides	Lecture	Course Content
Introduction [no overlays]	1 (Birch and Cohen)
Machine Translation Handout: Translation Exercise	2 (Birch)	Background reading: Automating Knowledge Acquisition for Machine Translation, Knight.
Conditional Language Models (with n-grams) [no overlays]	3 (Birch)	Background reading: Revision, in case you need to brush up on your probability: Basic probability theory, Goldwater. Revision, in case you want to brush up on n-gram language models: Neural Machine Translation and Sequence-to-sequence Models: A Tutorial, Neubig. Read up through the end of Section 3.

There are now many introductions to the basics of neural networks for NLP, which we'll focus on during the first few weeks of the course. What matters is that you understand the underlying concepts, rather than a particular author's notation. Everyone learns differently, so if you prefer a different take on perceptrons, feedforward models, recurrent networks, or attention, you may want to consult some of these resources in addition to (or as alternative to) the required readings:

Speech and Language Processing (3rd edition draft), Jurafsky & Martin.
Natural Language Processing, Eisenstein.
Deep Learning, Goodfellow et al.

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