# Annie Louis: List of Tutorials

I update this page with NLP/ML tutorials, surveys and articles that I have read and found useful.

### Some notes I have written

- Background for understanding Adaptor Grammars.

### Interesting courses

Some courses at different universities that I think are exciting. Here are their reading lists.

- Bayesian Non-parametrics : University of Maryland, Spring 2013. Taught by Jordan Boyd-Graber, Hal Daume III and Naomi Feldman.
- Linguistic Prediction: Another unique course from University of Maryland (2014) looking at aspects of language prediction in the brain and computational models. Taught by Hal Daume III, Naomi Feldman and Ellen Lau.
- Algorithms for NLP: Taught at CMU by Alon Lavie, Chris Dyer, and Bob Frederking and previously by Noah Smith.
- History of Computational Linguistics: At Stanford (2011) by Dan Jurafsky.

### Machine Learning and Background Math

- I've recently been finding Metacademy very useful in this regard. It tells you the background concepts for each technique and points to a variety of book, tutorial and video resources for any topic. Try it out [link]
- Jacob Eisenstein's reading list (many pointers to books)
- Generalized Linear Classifiers in NLP by Ryan McDonald.
- Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression chapter from Tom Mitchell's Machine Learning book.

### Bayesian Inference

Some resources which helped me learn about Bayesian Inference.

- Christopher Bishop's talk at Machine Learning Summer School. The nuts and bolts to understand bayesian methods and graphical models in general.
- Shay Cohen's book (2016) Bayesian Analysis in Natural Language Processing
- A light-hearted and easy overview by Kevin Knight - Bayesian Inference with Tears
- Sharon Goldwater's reading list
- A list of papers using Bayesian methods compiled by students in Shay Cohen's class at Columbia Univ. Also see the readings for that class Bayesian analysis for NLP
- A reading list from University of Maryland's course on Bayesian Nonparametrics. Taught by Jordan Boyd-Graber, Hal Daume III and Naomi Feldman.

### Topic models

- Video lecture and slides by David Blei at Machine Learning Summer School, 2009 at Cambridge UK. In fact, this summer school has a lot of other useful lectures, though I have not watched them all.
- An introductory tutorial by Mark Steyvers and Tom Griffiths.

### Applications

- Automatic Summarization by Ani Nenkova and Kathy McKeown.
- A Statistical MT Workbook by Kevin Knight for intro/IBM models.