Jordan Boyd-Graber

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): Starting Feb. 4, Mondays 15:00 - 16:00 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]
	Readings: SNLP 23
Wed 27. Mar	Homework Lab
Mon 1. Apr	Part of Speech Tagging / HMMs		[Video] [A B C]
	Readings: SLP Chapter 8
Wed 2. Apr	Constituency Parsing / PCFGs		[Video] [Slides Ex]
	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 (Faculty)
Fri 12. Apr	Homework Due	Project Proposal Due
Mon 15. Apr	Machine Translation		[Video: Word-Based Phrase Neural] [PDF: Word Phrase Neural Ex]
	Readings: NLP Notes Chapter 18
Wed 17. Apr	Topic Models		[Video: Intro Evaluation Gibbs Sampling] [PDF: Topic Models Gibbs Sampling Ex]
	Readings: Chapter 1 of "Applications of Topic Models"
Mon 22. Apr	Exercise Catchup		(No new material)
Wed 24. Apr	Project Workshop
Fri 26. Apr	Homework Due	First Deliverable
Mon 29. Apr	Computational Social Science
	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
Wed 8. May	Midterm II
Mon 13. May	Expo Match III (Surprise)
Mon 20. May 16:00-18:00	Final Presentations

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