Calendar

The calendar provides lecture topics, reading assignments, and laboratory project start and due dates. The reading assignments refer to the two recommended textbooks for the course.

Class days in blue, laboratory due dates including final project  in red, holidays in green, reg/add/drop dates in bold)
      February                 March               April                  May      
Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa
1 1 2 3 4 5 6 1 2 3 1
2 3 4 5 6 7 8 7 8 9 10 11 12 13 4 5 6 7 8 9 10 2 3 4 5 6 7 8
9 10 11 12 13 14 15 14 15 16 17 18 19 20 11 12 13 14 15 16 17 9 10 11 12 13 14 15
16 17 18 19 20 21 22 21 22 23 24 25 26 27 18 19 20 21 22 23 24 16 17 18 19 20 21 22
23 24 25 26 27 28 29 28 29 30 31 25 26 27 28 29 30 23 24 25 26 27 28 29
30 30 31

B:  Berwick, R., Natural Language Processing: A Laboratory Approach.
J&M: Jurafsky and Martin, Artificial Intelligence: A Modern Approach (2nd edition). Paramus: Prentice Hall PTR, 2002.









WEEK #


MONDAY



WEDNESDAY


LAB ASSIGNMENT















1  February 2-







Introduction

No Recitation
















2 February



Search
N - Chp 8
R&N - Chp 3
PS 1 due




Search
N - Chp 9
R&N - Chp 4


Recitation (PS 2)
















3


Constraint Satisfaction
N - Chp 11
R&N - Chp 5
PS 2 due




Games
N - Chp 12
R&N - Chp 6



Recitation (PS 3)
















4


Propositional Logic
N - Chp 13
R&N - Sec 7.4-6
PS 3 due




Propositional Logic
N - Chp 13
R&N - Sec 7.4-6



Recitation (PS 4)
















5 March



First Order Logic
N - Chp 15
R&N - Chp 8
PS 4 due




First Order Logic
N - Chp 15
R&N - Chp 8



Recitation (PS 5)
















6


Logical Proof
N - Chp 14, 16
R&N - Chp 9
PS 5 due




Logical Proof
N - Chp 14, 16
R&N - Chp 9



Recitation (PS 6)
PS 6 due
















7


Quiz 1




Rules
N - Sec 17.4
R&N Sec 9.3-4



Recitation (PS 7)















8


Language
R&N - Chp 22,23
PS 7 due




 



 
















9


 




Language
R&N - Chp 22,23



Recitation (PS 8)
















10


Machine Learning
R&N - Chp 18
PS 8 due




Decision Trees Naive Bayes
R&N - Chp 18



Recitation (PS 9)
















11


Continuous Features
R&N - Chp 19
PS 9 due




Quiz 2



Recitation (PS 10)
















12






Linear Separators
R&N - Chp 19
PS 10 due



Recitation (PS 11)
















13


SVM
R&N - Chp 19
PS 11 due




Neural Nets
R&N - Chp 19


 
















14


 




 



Recitation (PS 12)
















15


Neural Nets
R&N - Chap 19
PS 12 due




Feature & Model Selection
R&N - Chap 19



Recitation (PS 13)
PS 13 due
















16


Learning Wrapup
R&N - Chp 19




Philosophy

















17


Final Team Project Due