Algebraic Methods for Data Science  095295





Lecture 
Exercise 
Laboratory 
Project or Seminar 
House Work 
Weekly Hours 
3 
1 




Determination of the grade according to progress during the semester and a final examination.
Prerequisites:
 
(
  Discrete Mathematics (for I.E) 
094345
 
  
and
 Calculus 2M 
104032
 
  
and
 Algebra a 
104166
 
  
and
 Introduction to Computer Science H 
234117
 ) 

or

(
  Discrete Mathematics 
094346
 
  
and
 Linear Algebra M 
104019
 
  
and
 Calculus 2N 
104020
 
  
and
 Introduction to Computer Science 
234111
 ) 

or

(
  Discrete Mathematics 
094346
 
  
and
 Algebra 1/Extended 
104016
 
  
and
 Calculus 2N 
104020
 
  
and
 Introduction to Computer Science N 
234221
 ) 

or

(
  Discrete Mathematics 
094346
 
  
and
 Linear Algebra M 
104019
 
  
and
 Calculus 2N 
104020
 
  
and
 Introduction to Computer Science N 
234221
 ) 

Overlapping Courses:
    Numerical Algorithms 
234125
 
Complementary Topics in Linear Algebra (Inner Product Spaces, Norms, Orthonormal Bases, EigenValues), Matrix Decomposition (Spectral Decomposition Theorem, Svd), Linear Systems (Gauss Elimination Process), Lest Squares, Iterative Methods (Power Method), Linear Programming (Simplex Method, Duality, Applications to Classification and Sparse Solutions to Linear Systems), Perturbation Theorems for Matrix Decomposition, Introduction to Tensors.
TextbooksPublished  Publisher  Authors  Book 

2013  the johns hopkins uni. press  g. golun and c. van loan  matrix computation 
2014  cambridge uni. press  g. calafiore and l. el ghaoui  optimization models 
Created in 22/04/2021 Time 15:22:46