Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
courses:cs366:schedule [2020/01/19 13:22]
ide
courses:cs366:schedule [2020/01/20 11:56]
ide [Table]
Line 6: Line 6:
 Lecture slides in PDF are obtained by clicking on the link to the topic.// Lecture slides in PDF are obtained by clicking on the link to the topic.//
  
-//Homeworks (prefixed with "HW") are exercises that you should complete before the class labeling the row in which they appear. These will not be handed in or graded, but provide background to the lecture and it will be assumed you have completed them. The due date for each of the four large (graded) assignments (prefixed with "A") will be specified in the assignment description.//+//Homeworks (prefixed with "HW") are exercises that you should complete for the class **following** the one labeling the row in which they appear. In some cases we will begin the homework in class; therefore, it is imperative that you do the reading before the class where the homework is assigned. Homeworks will not be graded, but they must be handed in to GitHub classroom. The due date for each of the four large (graded) assignments (prefixed with "A") will be specified in the assignment description.//
  
 **Please note that this schedule is tentative and may change as the semester progresses. Please visit this page often!** **Please note that this schedule is tentative and may change as the semester progresses. Please visit this page often!**
  
  
-^ Date       ^ Topic  ^ Reading  ^ Assignments ^ Supplemental materials ^ +^ Date  ^ Topic                                                                                                                                                                                   ^ Reading                                                               ^ Assignments                ^ Supplemental materials                                              
-| 1/22 | [[http://www.cs.vassar.edu/~cs366/private/lectures/intro.pdf| Introduction]] [[http://www.cs.vassar.edu/~cs366/private/lectures/intro.pptx|(PPT)]] | J&M Ch. 1 | | |                                                                                                                                                                            +| 1/22  | [[http://www.cs.vassar.edu/~cs366/private/lectures/intro.pdf| Introduction]] [[http://www.cs.vassar.edu/~cs366/private/lectures/intro.pptx|(PPT)]]                                      | J&M Ch. 1                                                                                                                                                            
-| 1/27       | [[http://www.cs.vassar.edu/~cs366/private/lectures/morphology.pdf| Finite state transducers, morphology, and general text processing]][[http://www.cs.vassar.edu/~cs366/private/lectures/morphology.pptx| (PPT)]]          | NLTK Ch. 1, J&M Ch. 3 pp. 45-68  |  [[https://www.cs.upc.edu/~padro/Unixforpoets.pdf|HW: UNIX for Poets]] Due               +| 1/27  | [[http://www.cs.vassar.edu/~cs366/private/lectures/morphology.pdf| Morphology and general text processing]][[http://www.cs.vassar.edu/~cs366/private/lectures/morphology.pptx| (PPT)]]  | NLTK Ch. 1, J&M Ch. 3 pp. 45-68                                       |  HW: Ex. 2.2, J&         |  [[https://www.cs.upc.edu/~padro/Unixforpoets.pdf|UNIX for Poets]]  
-2/3       | [[http://www.cs.vassar.edu/~cs366/private/lectures/intro-nltk.pdf| Introduction to NLTK and Python ]] [[http://www.cs.vassar.edu/~cs366/private/lectures/intro-nltk.pptx|(PPT) ]] | NLTK Ch. 3, 4         |[[https://docs.python.org/3/tutorial/| Python3.5 tutorial]]                           +1/29  | [[http://www.cs.vassar.edu/~cs366/private/lectures/edit-distance.pdf| Minimum Edit Distance]][[http://www.cs.vassar.edu/~cs366/private/lectures/edit-distance.pptx| (PPT)]]             J&Ch. 3.11                                                          HWJ&M Ex3.10 and 3.11  |                                                                     
-| 2/5        | [[http://www.cs.vassar.edu/~cs366/private/lectures/machine-learning.pdf|Introduction to Machine Learning]]                                                                                        [[http://www.cs.cmu.edu/~tom/mlbook/keyIdeas.pdfKey Ideas in Machine Learning]]                                                                                                                                                                           +| 2/3   | [[http://www.cs.vassar.edu/~cs366/private/lectures/ngrams.pdf|Language models, probabilistic approaches, n-grams]]                                                                      J&M Ch4 (through 4.5.2), NLTK Ch2                                                                                                                                | 
-| 2/10       |Machine learning (con't): Naive Bayes and sentiment analysis   | [[https://web.stanford.edu/~jurafsky/slp3/4.pdf|J&Edition 3, Chapter 4]]    +| 2/5   | Ngram models, smoothing, discounting                                                                                                                                                    | J&M Ch. 4.5-4.9, 4.12                                                                            |                                                                     
-| 2/12       | [[http://www.cs.vassar.edu/~cs366/private/lectures/ngrams.pdf|Language models, probabilistic approaches, n-grams]] | J&M Ch. 4 (through 4.5.2), NLTK Ch. 2 |       | +| 2/10  | Naive Bayes and Text Classification                                                                                                                                                     | [[http://web.stanford.edu/~jurafsky/slp3/4.pdf|J&(3ed) Chapter 4]]  |                                                                                                
-| 2/17       | [[http://www.cs.vassar.edu/~cs366/private/lectures/weka.pdf| Weka overview]] |[[http://www.cs.vassar.edu/~cs366/docs/Data-Mining-Weka.pdf|Data Mining, Ch. 11.1-4, 11.6-8]] |  |[[http://www.cs.vassar.edu/~cs366/docs/weka-tutorial-full.pdf|Weka Tutorial]] [[http://www.cs.vassar.edu/~cs366/docs/WekaManual-3-9-2.pdf|Weka Manual]] | |                                                                                                    +| 2/12  | [[http://www.cs.vassar.edu/~cs366/private/lectures/ngrams.pdf|Language models, probabilistic approaches, n-grams]]                                                                      | J&M Ch. 4 (through 4.5.2), NLTK Ch. 2                                                             [[https://www.cs.upc.edu/~padro/Unixforpoets.pdf|UNIX for Poets]]  | 
-| 2/19       | Automatically determining word meaning: word embeddings | [[https://web.stanford.edu/~jurafsky/slp3/6.pdf|J&M ch.6 (3rd ed.), NLTK Ch. 6]]  +| 2/17  | [[http://www.cs.vassar.edu/~cs366/private/lectures/intro-nltk.pdf| Introduction to NLTK and Python ]] [[http://www.cs.vassar.edu/~cs366/private/lectures/intro-nltk.pptx|(PPT) ]]       NLTK Ch. 3, 4, 5                                                                                 | [[https://docs.python.org/3/tutorial/| Python3 tutorial]]           
-+| 2/19  TBA                                                                                                                                                                                                                                                                                      |                                                                     | 
-| 2/26       [[http://www.cs.vassar.edu/~cs366/private/lectures/sentiment-analysis.pdf|Sentiment Analysis]]                                                                                     | [[https://web.stanford.edu/~jurafsky/slp3/6.pdf|J&M ch. 6 (3rd ed.)]], [[https://web.stanford.edu/~jurafsky/slp3/18.pdf|J&M ch. 18 (3rd ed.)]] [[http://www.cs.vassar.edu/~cs366/docs/NLP-handbook-sentiment-analysis.pdf| R1]]                         | /*                       [[http://www.cs.vassar.edu/~cs366/private/assignments/assn02.pdf|A2]]  */  | /*[[http://www.cs.vassar.edu/~cs366/private/lectures/sentiment-analysis-assignment.pdf|A2 supplement]]*/      |+s366/private/lectures/sentiment-analysis.pdf|Sentiment Analysis]]                                                                                     | [[https://web.stanford.edu/~jurafsky/slp3/6.pdf|J&M ch. 6 (3rd ed.)]], [[https://web.stanford.edu/~jurafsky/slp3/18.pdf|J&M ch. 18 (3rd ed.)]] [[http://www.cs.vassar.edu/~cs366/docs/NLP-handbook-sentiment-analysis.pdf| R1]]                         | /*                       [[http://www.cs.vassar.edu/~cs366/private/assignments/assn02.pdf|A2]]  */  | /*[[http://www.cs.vassar.edu/~cs366/private/lectures/sentiment-analysis-assignment.pdf|A2 supplement]]*/      |
 | 3/5        | [[http://www.cs.vassar.edu/~cs366/private/lectures/information-extraction.pdf|Information extraction, named entity recognition]]                                                  | [[https://web.stanford.edu/~jurafsky/slp3/21.pdf|J&M Ch. 21, 3rd edition]] | 3/5        | [[http://www.cs.vassar.edu/~cs366/private/lectures/information-extraction.pdf|Information extraction, named entity recognition]]                                                  | [[https://web.stanford.edu/~jurafsky/slp3/21.pdf|J&M Ch. 21, 3rd edition]]
 | /* [[http://www.cs.vassar.edu/~cs366/private/assignments/assn03.pdf|A3]] [[https://www.cs.vassar.edu/~cs366/private/solutions/assn03| S3]] */ | | /* [[http://www.cs.vassar.edu/~cs366/private/assignments/assn03.pdf|A3]] [[https://www.cs.vassar.edu/~cs366/private/solutions/assn03| S3]] */ |