Courses & Technical Notes:
Machine Learning & Optimization
- Deep Learning: Sequence-to-Sequence Models tutorial (2019)
- Deep Learning (out-of-date, older notes): Short Course (2014); Deep Learning for Machine Translation (2014);
- Multiobjective optimization: a survey.
- Brief notes on: MCMC, Bundle methods
- Statistical Learning Course at the University of Washington, co-taugh with Prof. Mari Ostendorf (2008)
- Data Mining: frequent itemset, sequence mining, graph mining
Natural Language Processing
- Fall 2019 course in Natural Language Processing (601.465/665)
- Intro to HLT Omnibus Course: Machine Translation, Information Retrieval, Question Answering
- Domain Adaptation for Neural Machine Translation (tutorial, MTMA2019)
- Intro to Information Retrieval and Web Search, Learning to Rank - Tutorial/Lab at JSALT2019
- "Building a Phrase-based Machine Translation System" tutorial (co-taught with Graham Neubig, 2012). Slides are in bitext format! [English], [Japanese]
- Factored Language Models: Intro slides, Tech Report tutorial.
- Linguistics: Generative Lexicon, Languages of the World
Research Career Tips:
I've been indebted to Rich Baraniuk's wonderful collection of advice since my undergrad years. Below is a selection of my favorites, including some newfound gems.- You and Your Research by Richard Hamming.
- How to Have a Bad Career in Research/Academia by David A. Patterson.
- How to Read a Technical Paper by Jason Eisner.
- How to Be a Successful PhD Student (in CS) by Mark Dredze and Hanna Wallach.
- Preventing Repetitive Straing Injury (RSI) by Clay Scott.
- Deep Work by Cal Newport.
- PhD Comics -- actually this is not recommended for research productivity; but it's just too funny.