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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.)
Help with the UW Time Schedule 

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