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 | Spring 2022 Time Schedule

Spring Quarter 2022 Time Schedule

LINGUISTICS
(COLLEGE OF ARTS AND SCIENCES )
(UW PROFESSIONAL AND CONTINUING EDUCATION )

Enrollment and status (open/closed) were accurate when this page was created (12:02 am June 17, 2022) but may have changed since then. For current enrollment and status, check the . (UW NetID required.)
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Enrl        Sect                                                                                             Crs
Restr   SLN  ID Cred    Meeting Times     Bldg/Rm       Instructor                 Status Enrl/Lim   Grades  Fee Other
 ?       ?       ?           ?             ?               ?                         ?       ?         ?      ?    ?

LING   573  NLP SYSTEMS/APPS
Restr    322      Levow,Gina-Anne            Open     28/  31                J     
                        ON CAMPUS SECTION FOR CLMS STUDENTS                         
Restr    322      Levow,Gina-Anne            Open     24/  25                J     
                        ONLINE SECTION FOR CLMS STUDENTS                            

LING   575  COMPUT LING TOPICS
      >  115      Mitchell,Margaret A                  3/   8                J     
                        VALUE SENSITIVE DATA PROCESSING                             
THIS COURSE WILL COVER THE
CONSTRUCTION, ANALYSIS, AND MANAGEM
OF DATA USED TO TRAIN AND EVALUATE
MODELS. THE COURSE WILL HAVE
READINGS, VIDEOS, AND LECTURES ON
GENERAL TECHNIQUES FOR DATA
COLLECTION, ANNOTATION AND PROCESSI
WITH A FOCUS ON THE VALUES THAT
MAY BE ENCODED THROUGHOUT THE DATA
DEVELOPMENT PIPELINE. STUDENTS'
FINAL PROJECT WILL BE A RIGOROUS
EVALUATION DATASET FOR AN ML MODEL.
ON CAMPUS SECTION FOR CLMS STUDENTS
      >  115      Mitchell,Margaret A                  8/  10                J     
                        VALUE SENSITIVE DATA PROCESSING                             
THIS COURSE WILL COVER THE
CONSTRUCTION, ANALYSIS, AND MANAGEM
OF DATA USED TO TRAIN AND EVALUATE
MODELS. THE COURSE WILL HAVE
READINGS, VIDEOS, AND LECTURES ON
GENERAL TECHNIQUES FOR DATA
COLLECTION, ANNOTATION AND PROCESSI
WITH A FOCUS ON THE VALUES THAT
MAY BE ENCODED THROUGHOUT THE DATA
DEVELOPMENT PIPELINE. STUDENTS'
FINAL PROJECT WILL BE A RIGOROUS
EVALUATION DATASET FOR AN ML MODEL.
ONLINE SECTION FOR CLMS STUDENTS.
Restr    202      Levow,Gina-Anne            Closed    8/   8                J     
                        SPEECH TECHNOLOGY FOR ENDANGERED                            
LANGUAGES
THE COURSE WILL COVER THE THEORY AN
PRACTICE OF SPEECH TECHNOLOGY AND
ITS APPLICATION TO ENDANGERED
LANGUAGES. THE COURSE WILL HAVE
READINGS AND LECTURES ON GENERAL
TECHNIQUES AND ISSUES IN SPEECH
TECHNOLOGY AND WILL USE PUBLICLY
AVAILABLE TOOLS AND TOOLKITS TO
INVESTIGATE THE APPLICATION OF SPEE
TECHNOLOGY TO ENDANGERED LANGUAGE
DATA.
ON CAMPUS SECTION FOR CLMS.
Restr    202      Levow,Gina-Anne            Open      6/   7                J     
                        SPEECH TECHNOLOGY FOR ENDANGERED                            
LANGUAGES
THE COURSE WILL COVER THE THEORY AN
PRACTICE OF SPEECH TECHNOLOGY AND
ITS APPLICATION TO ENDANGERED
LANGUAGES. THE COURSE WILL HAVE
READINGS AND LECTURES ON GENERAL
TECHNIQUES AND ISSUES IN SPEECH
TECHNOLOGY AND WILL USE PUBLICLY
AVAILABLE TOOLS AND TOOLKITS TO
INVESTIGATE THE APPLICATION OF SPEE
TECHNOLOGY TO ENDANGERED LANGUAGE
DATA.
ONLINE SECTION FOR CLMS STUDENTS.
Restr    309      Steinert-Threlkeld,Shane N Closed   13/   8                J     
                        ANALYZING NEURAL LANGUAGE MODELS                            
TWO RECENT TRENDS IN NLP---THE
APPLICATION OF DEEP NEURAL NETWORKS
AND THE USE OF TRANSFER LEARNING---
HAVE RESULTED IN MANY MODELS THAT
ACHIEVE HIGH PERFORMANCE ON IMPORTA
TASKS BUT WHOSE BEHAVIOR ON
THOSE TASKS IS DIFFICULT TO INTERPR
IN THIS SEMINAR, WE WILL LOOK
AT METHODS INSPIRED BY LINGUISTICS
COGNITIVE SCIENCE FOR ANALYZING
WHAT LARGE NEURAL LANGUAGE MODELS H
IN FACT LEARNED:
DIAGNOSTIC/PROBING CLASSIFIERS,
ADVERSARIAL TEST SETS, AND ARTIFICI
LANGUAGES, AMONG OTHERS. PARTICULAR
ATTENTION WILL BE PAID TO PROBING
THESE MODELS' _SEMANTIC_ KNOWLEDGE,
WHICH HAS RECEIVED COMPARABLY
LITTLE ATTENTION COMPARED TO THEIR
SYNTACTIC KNOWLEDGE. STUDENTS WILL
ACQUIRE RELEVANT SKILLS AND (IN SMA
GROUPS) DESIGN AND EXECUTE A
LINGUISTICALLY-INFORMED ANALYSIS
EXPERIMENT, RESULTING IN A REPORT I
THE FORM OF A PUBLISHABLE CONFERENC
PAPER.
ON CAMPUS SECTION FOR CLMS
STUDENTS.
Restr    309      Steinert-Threlkeld,Shane N Closed    8/   7                J     
                        ANALYZING NEURAL LANGUAGE MODELS                            
TWO RECENT TRENDS IN NLP---THE
APPLICATION OF DEEP NEURAL NETWORKS
AND THE USE OF TRANSFER LEARNING---
HAVE RESULTED IN MANY MODELS THAT
ACHIEVE HIGH PERFORMANCE ON IMPORTA
TASKS BUT WHOSE BEHAVIOR ON
THOSE TASKS IS DIFFICULT TO INTERPR
IN THIS SEMINAR, WE WILL LOOK
AT METHODS INSPIRED BY LINGUISTICS
COGNITIVE SCIENCE FOR ANALYZING
WHAT LARGE NEURAL LANGUAGE MODELS H
IN FACT LEARNED:
DIAGNOSTIC/PROBING CLASSIFIERS,
ADVERSARIAL TEST SETS, AND ARTIFICI
LANGUAGES, AMONG OTHERS. PARTICULAR
ATTENTION WILL BE PAID TO PROBING
THESE MODELS' _SEMANTIC_ KNOWLEDGE,
WHICH HAS RECEIVED COMPARABLY
LITTLE ATTENTION COMPARED TO THEIR
SYNTACTIC KNOWLEDGE. STUDENTS WILL
ACQUIRE RELEVANT SKILLS AND (IN SMA
GROUPS) DESIGN AND EXECUTE A
LINGUISTICALLY-INFORMED ANALYSIS
EXPERIMENT, RESULTING IN A REPORT I
THE FORM OF A PUBLISHABLE CONFERENC
PAPER.
ONLINE SECTION FOR CLMS STUDENTS.
Restr    407      Steinert-Threlkeld,Shane N Open     22/  25                J     
                        DEEP LEARNING FOR NATURAL LANGUAGE                          
PROCESSING
THE APPLICATION OF NEURAL NETWORK
METHODS---UNDER THE NAME "DEEP
LEARNING"---HAS LED TO BREAKTHROUGH
IN A WIDE RANGE OF FIELDS, INCLUDIN
IN BUILDING LANGUAGE TECHNOLOGIES
(E.G. FOR SEARCH, TRANSLATION, TEXT
INPUT PREDICTION). THIS COURSE WILL
PROVIDE A HANDS-ON INTRODUCTION TO
USE OF DEEP LEARNING METHODS FOR
PROCESSING NATURAL LANGUAGE. METHOD
TO BE COVERED INCLUDE STATIC WORD
EMBEDDINGS, CONVOLUTIONAL NEURAL
NETWORKS FOR TEXT, RECURRENT NEURAL
NETWORKS, TRANSFORMERS, PRE-TRAININ
AND TRANSFER LEARNING, WITH
APPLICATIONS INCLUDING PARSING,
TRANSLATION, AND GENERATION.
ON CAMPUS SECTION FOR CLMS
STUDENTS.
Restr    407      Steinert-Threlkeld,Shane N Open     14/  20                JO    
                        DEEP LEARNING FOR NATURAL LANGUAGE                          
PROCESSING
THE APPLICATION OF NEURAL NETWORK
METHODS---UNDER THE NAME "DEEP
LEARNING"---HAS LED TO BREAKTHROUGH
IN A WIDE RANGE OF FIELDS, INCLUDIN
IN BUILDING LANGUAGE TECHNOLOGIES
(E.G. FOR SEARCH, TRANSLATION, TEXT
INPUT PREDICTION). THIS COURSE WILL
PROVIDE A HANDS-ON INTRODUCTION TO
USE OF DEEP LEARNING METHODS FOR
PROCESSING NATURAL LANGUAGE. METHOD
TO BE COVERED INCLUDE STATIC WORD
EMBEDDINGS, CONVOLUTIONAL NEURAL
NETWORKS FOR TEXT, RECURRENT NEURAL
NETWORKS, TRANSFORMERS, PRE-TRAININ
AND TRANSFER LEARNING, WITH
APPLICATIONS INCLUDING PARSING,
TRANSLATION, AND GENERATION.
ONLINE SECTION FOR CLMS STUDENTS

LING   600  INDEPNDNT STDY/RSCH
 IS     MASTERS THESIS
 IS    
Modified: June 17, 2022