MA 751 Supplementary materials
 
Course Announcement -     
Course information and notes – 
Syllabus
Projects
Notes on matrix notation
Midterm Solutions (Spring 2021)
Note on Bayesian statistics (Section 8.3)
Midterm Solutions (Spring 2022)
Final Exam Solutions (Spring 2021)
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                Problem set  | 
  
   Due date  | 
 
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       Feb. 3  | 
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      Feb. 10  | 
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       Feb. 17  | 
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        Feb. 24  | 
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       March
  1  | 
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       March
  4  | 
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       March
  17  | 
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   █   Midterm test  | 
  
       March
  24  | 
 
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       March
  31  | 
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       April
  5  | 
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       April
  8  | 
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      April
  14  | 
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      April 21  | 
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       April
  28  | 
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   (Note PS 11 does need not to be turned in)  | 
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      May
  4  | 
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   (Note PS 12 parts 1 and 2 need not be turned in)  | 
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      May
  4  | 
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   Lecture  | 
  
   Lecture Notes  | 
  
   Day  | 
 
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   Introduction  | 
  
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   Introduction  | 
  
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   Lecture
  1  | 
  
       Thursday  | 
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   Lecture
  2  | 
  
       Tuesday  | 
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   Lecture
  3  | 
  
       Thursday  | 
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   Lecture
  4  | 
  
       Tuesday  | 
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   Lecture
  5  | 
  
       Thursday  | 
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   Lecture
  6  | 
  
   █   Feb. 8  | 
  
       Tuesday  | 
 
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   Lecture
  7  | 
  
   █   Feb. 10  | 
  
       Thursday   | 
 
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   Lecture
  8  | 
  
       Tuesday  | 
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   Lecture
  9  | 
  
       Thursday  | 
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   Lecture
  10  | 
  
       Thursday  | 
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   Lecture
  11  | 
  
   █   Mar. 1  | 
  
       Tuesday  | 
 
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   Lecture
  12  | 
  
   █   Mar. 3  | 
  
       Thursday  | 
 
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   Lecture
  13  | 
  
   █   Mar. 15  | 
  
       Tuesday  | 
 
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   Lecture
  14  | 
  
   █   Mar. 17  | 
  
       Thursday  | 
 
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   Lecture
  15  | 
  
   █   Mar. 22  | 
  
       Tuesday  | 
 
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   Test  | 
  
   █   Mar. 24  | 
  
       Thursday  | 
 
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   Lecture
  16  | 
  
   █   Mar. 29  | 
  
       Tuesday  | 
 
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   Lecture
  17  | 
  
   █   Mar. 31  | 
  
       Thursday  | 
 
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   Lecture
  18  | 
  
   █   Apr. 5  | 
  
       Tuesday  | 
 
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   Lecture
  19  | 
  
   █   Apr. 7  | 
  
       Thursday  | 
 
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   Lecture
  20  | 
  
   █   Apr. 12  | 
  
       Tuesday  | 
 
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   (Note the first movie
  shows how Gaussian kernel width affects SVM classification - kernel width
  narrows with time.  Second movie shows
  how changing the margin parameter C affects the classification – margin
  narrows with time)  | 
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   Lecture
  21  | 
  
   █   Apr. 14  | 
  
       Thursday  | 
 
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   Lecture
  22  | 
  
   █   Apr. 19  | 
  
       Tuesday  | 
 
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   Lecture
  23  | 
  
   █   Apr. 21  | 
  
       Thursday  | 
 
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   Lecture
  24  | 
  
   █   Apr. 26  | 
  
       Tuesday  | 
 
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   Lecture
  25  | 
  
   █   Apr. 28  | 
  
       Thursday  | 
 
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   Lecture
  26  | 
  
   █   May 3  | 
  
       Tuesday  | 
 
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   Lecture
  11C  | 
  
   Optional lecture: not covered in  problem sets or exams  | 
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   Lecture
  11D  | 
  
   Optional lecture: not covered in  problem sets or exams  | 
 
Optional additional material and examples 
2. Decision trees and random forests
Lecture Notes -  
1.  The three
pillars of machine learning
2. Probability and Measure Theory
5. 
Statistical machine learning and infinite dimensions
6.  Measure
spaces and Hilbert spaces
7.  Reproducing
kernel Hilbert Spaces
8.  Support
vector machines (SVM) (optional)
Optional additional material and examples 
Resources – 
Neural networks: Funahashi’s Theorem