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Jordan Boyd-Graber
4146 Iribe
Computer Science
jbg@boydgraber.org

Natural Language Processing (CMSC 470)

Logistics

Location	Iribe 2207
Time	Mon./Wed. 17:00pm - 18:15pm
Webpage	http://umiacs.umd.edu/~jbg/teaching/CMSC_470/
Mailing List	http://piazza.com/umd/spring2019/cmsc470
Text	Speech and Natural Language Processing
Syllabus	https://docs.google.com/document/d/1UN3qlJttJFRXP4qtoIriZxHjYQflvsUNsulHLIFVTzQ/edit?usp=sharing

People

Professor

Jordan Boyd-Graber
IRB 4146
Office Hours (IRB 4146): Mondays 12:00 - 13:00 and by appointment

Teaching Assistant

Pranav Goel: IRB 4134, Thursday 16:00-17:00, Friday 15:00-16:00

Schedule

Date	In-Class Topic	Assignment Due	Lecture
Mon 28. January	Introduction to the course, Probability, and Python		[Video: A B] [PDF: A B]
	Optional Readings: Python (and programming) introductory course You may also want to look at first couple of weeks (through February) of INST 414 as review. A probability primer (Optional, if you want more detail) Grinstead and Snell, Chapters 1-2, Chapter 4.1 How the statistical revolution changes (computational) linguistics On the deep learning revolution in NLP
Wed 30. Jan	Information Retrieval / tf-idf		[Video: A B C] [PDF: A B C D] [Class]
	Readings: IR Chapter 1, 6
Mon 4. Feb	Logistic Regression and Naive Bayes		[PDF A B C] [Video A B] [Class]
	Readings: SNLP Chapter 5.1-5.3
Wed 6. Feb	Homework Lab
Fri 8. Feb	Homework Due	tf-idf
Mon 11. Feb	Project Framework / Codalab		[Video: A B C] [PDF: A B C] [Class]
	Readings: QANTA.org
Wed 13. Feb	Stochastic Gradient Descent		[PDF A B] [Video A] [Class A Class B]
	Readings: SNLP Chapter 5.4-5.9
Fri 15. Feb	Homework Due	NN Question Answering
Mon 18. Feb	Neural Networks for Language		[Video: Deep Backprop Frameworks Pytorch DAN] [PDF: A B C D E F] [Class]
	Readings: SNLP 7
Wed 20. Feb	Totally Unnecessary Snow Day
	Readings: Tutorial Tons of NLP Models
Mon 25. Feb	Distributional Semantics (Word2Vec)		[Video: A B C] [PDF: A B C D] [Class]
	Readings: SNLP Chapter 6
Wed 27. Feb	Expo Match I (Students)		[Class]
Fri 1. Mar	Homework Due	Logistic Regression
Mon 4. Mar	Classification and Feature Engineering		[Video: Classification, Examples: A B] [PDF: Classification Examples: A B] [Class]
	Readings: SNLP 4
Wed 6. Mar	Neural Sequence Models		[PDF: A B C] [Video: RNN LSTM] [Class]
	Readings: SNLP 9
Fri 8. Mar	Homework Due	Deep Averaging Networks
Mon 11. Mar	Midterm Review
Wed 13. Mar	Midterm
Mon 25. Mar	Question Answering		[Video: Entity Recognition and Linking Coref Datasets Dr. QA] [PDF: A B C D E] [In-class]
	Readings: SNLP 23
Wed 27. Mar	Homework Lab
Mon 1. Apr	Part of Speech Tagging / HMMs		[Video] [A B C] [In-class]
	Readings: SLP Chapter 8
Wed 3. Apr	Constituency Parsing / PCFGs		[Video] [Slides Ex] [In-class]
	Readings: SLP Chapter 12
Fri 5. Apr	HW Due	Sequence Buzzer
Mon 8. Apr	No Class: Meet to Discuss Project		[Sign Up]
Wed 10. Apr	Expo Match II (MAQT / Faculty)		[In-class]
Fri 12. Apr	Homework Due	Project Proposal Due
Mon 15. Apr	Machine Translation		[Video: Word-Based Phrase Neural] [PDF: Word Phrase Neural Ex] [In-class]
	Readings: NLP Notes Chapter 18
Wed 17. Apr	Topic Models		[Video: Intro Evaluation Gibbs Sampling] [PDF: Topic Models Gibbs Sampling Ex] [In-class]
	Readings: Chapter 1 of "Applications of Topic Models"
Mon 22. Apr	Exercise Catchup		[In-class]
Wed 24. Apr	Project Workshop
Fri 26. Apr	Homework Due	First Deliverable
Mon 29. Apr	Computational Social Science		[ Sentiment Ideology]
	Readings: Overview Text as Data
Wed 1. May	AMA
	Readings: The Seven Deadly Sins of AI Predictions How Computers Misunderstand the World
Mon 6. May	Midterm Review		[In Class Video]
Wed 8. May	Midterm II
Mon 13. May	Expo Match III (Surprise)
Mon 20. May 16:00-18:00	Final Presentations

Page by Jordan Boyd-Graber.