  01  MWF  3:00P4:00P  TBA  [TBA]  May 2 2019 6:00PM  8:00PM  45  0  0   

  01  MWF  9:00A10:00A  TBA  Kuehne  May 2 2019 3:30PM  5:30PM  75  0  0   
 A  R  9:00A10:00A  TBA  [TBA]  Default  none  12  0  0   
 B  R  10:00A11:00A  TBA  [TBA]  Default  none  12  0  0   
 C  R  12:00P1:00P  TBA  [TBA]  Default  none  12  0  0   

  01  MWF  10:00A11:00A  TBA  Johnson  May 3 2019 10:30AM  12:30PM  125  0  0   
 02  MWF  11:00A12:00P  TBA  Johnson  May 3 2019 10:30AM  12:30PM  130  0  0   
 A  T  8:00A9:00A  TBA  [TBA]  Default  none  15  0  0   
 B  T  9:00A10:00A  TBA  [TBA]  Default  none  15  0  0   
 C  T  9:00A10:00A  TBA  [TBA]  Default  none  15  0  0   
 D  T  10:00A11:00A  TBA  [TBA]  Default  none  15  0  0   
 E  T  10:00A11:00A  TBA  [TBA]  Default  none  15  0  0   
 F  T  11:00A12:00P  TBA  [TBA]  No Final  15  0  0   
 G  T  11:00A12:00P  TBA  [TBA]  No Final  15  0  0   
 H  T  12:00P1:00P  TBA  [TBA]  No Final  15  0  0   
 I  T  12:00P1:00P  TBA  [TBA]  No Final  15  0  0   

  01  MTWRF  11:00A12:00P  TBA  Kumar  May 7 2019 10:30AM  12:30PM  30  0  0   

  01  MWF  9:00A10:00A  TBA  Krantz  May 3 2019 10:30AM  12:30PM  125  0  0   
 02  MWF  11:00A12:00P  TBA  Krantz  May 3 2019 10:30AM  12:30PM  130  0  0   

 Description:  An elementary introduction to statistical concepts, reasoning and data analysis. Topics include statistical summaries and graphical presentations of data, discrete and continuous random variables, the logic of statistical inference, design of research studies, point and interval estimation, hypothesis testing, and linear regression. Students will learn a critical approach to reading statistical analyses reported in the media, and how to correctly interpret the outputs of common statistical routines for fitting models to data and testing hypotheses. A major objective of the course is to gain familiarity with basic R commands to implement common data analysis procedures. Students intending to pursue a major or minor in mathematics or wishing to take 400 level or above statistics courses should instead take Math 3200. Prerequisite: Math 131. EXAMINATION SCHEDULE: Tests, at which attendance is required, will be given from 6:30 to 8:30 p.m. on: Wednesday February 6, Wednesday March 6, and Wednesday April 10. 

  01  MWF  9:00A10:00A  TBA  Vittert  May 2 2019 3:30PM  5:30PM  100  0  0   
 02  MWF  11:00A12:00P  TBA  Vittert  May 2 2019 3:30PM  5:30PM  120  0  0   
 A  T  8:00A9:00A  TBA  [TBA]  Default  none  10  0  0   
 B  T  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 C  T  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 D  T  10:00A11:30A  TBA  [TBA]  Default  none  10  0  0   
 E  T  11:30A1:00P  TBA  [TBA]  Default  none  10  0  0   
 F  T  11:30A1:00P  TBA  [TBA]  Default  none  10  0  0   

  01  MWF  10:00A11:00A  TBA  Stern  May 2 2019 3:30PM  5:30PM  150  0  0   
 02  MWF  11:00A12:00P  TBA  Stern  May 2 2019 3:30PM  5:30PM  150  0  0   
 03  MWF  12:00P1:00P  TBA  Bongers  May 2 2019 3:30PM  5:30PM  195  0  0   
 A  R  8:00A9:00A  TBA  [TBA]  Default  none  10  0  0   
 B  R  8:00A9:00A  TBA  [TBA]  Default  none  10  0  0   
 C  R  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 D  R  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 E  R  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 F  R  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 G  R  10:00A11:30A  TBA  [TBA]  Default  none  10  0  0   
 H  R  10:00A11:30A  TBA  [TBA]  Default  none  10  0  0   
 I  R  10:00A11:30A  TBA  [TBA]  Default  none  10  0  0   
 J  R  11:30A1:00P  TBA  [TBA]  Default  none  10  0  0   
 K  R  11:30A1:00P  TBA  [TBA]  Default  none  10  0  0   
 L  R  11:30A1:00P  TBA  [TBA]  Default  none  10  0  0   

  01  MWF  2:00P3:00P  TBA  Bongers  May 6 2019 3:30PM  5:30PM  40  0  0   

 Description:  An introductory course in linear algebra that focuses on Euclidean nspace, matrices and related computations. Topics include: systems of linear equations, row reduction, matrix operations, determinants, linear independence, dimension, rank, change of basis, diagonalization, eigenvalues, eigenvectors, orthogonality, symmetric matrices, least square approximation, quadratic forms. Introduction to abstract vector spaces. Tests, at which attendance is required, will be given from 6:308:30 p.m. on Monday February 25, and Monday April 1.
Prerequisite: Math 132. 

  01  MWF  9:00A10:00A  TBA  Johnson  May 6 2019 10:30AM  12:30PM  80  0  0   
 02  MWF  11:00A12:00P  TBA  Shapiro  May 6 2019 10:30AM  12:30PM  120  0  0   
 03  MWF  12:00P1:00P  TBA  Shapiro  May 6 2019 10:30AM  12:30PM  150  0  0   

  01  MWF  12:00P1:00P  TBA  Frankel  May 8 2019 10:30AM  12:30PM  50  0  0   
 02  MWF  1:00P2:00P  TBA  Frankel  May 8 2019 1:00PM  3:00PM  60  0  0   

  01  MWF  2:00P3:00P  TBA  [TBA]  May 6 2019 3:30PM  5:30PM  85  0  0   
 02  MWF  3:00P4:00P  TBA  [TBA]  May 2 2019 6:00PM  8:00PM  85  0  0   

 Description:  AAn introduction to probability and statistics. Major topics include elementary probability, special distributions, experimental design, exploratory data analysis, estimation of mean and proportion, hypothesis testing and confidence, regression, and analysis of variance. Emphasis is placed on development of statistical reasoning, basic analytic skills, and critical thinking in empirical research studies. The use of the statistical software R is integrated into lectures and weekly assignments. Required for students pursuing a major or minor in mathematics or wishing to take 400 level or above statistics courses. Prereqs: Math 132. Though Math 233 is not essential, it is recommended. EXAMINATION SCHEDULE: Tests, at which attendance is required, will be given from 6:30 to 8:30 p.m. on: Tuesday February 5, Tuesday March 5, and Tuesday April 9. 

  01  MWF  9:00A10:00A  TBA  Syring  May 2 2019 3:30PM  5:30PM  75  0  0   
 02  MWF  10:00A11:00A  TBA  Syring  May 2 2019 3:30PM  5:30PM  75  0  0   
 A  T  8:00A9:00A  TBA  [TBA]  Default  none  10  0  0   
 B  T  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 C  T  9:00A10:00A  TBA  [TBA]  Default  none  10  0  0   
 D  T  10:00A11:30A  TBA  [TBA]  Default  none  10  0  0   
 E  T  11:30A1:00P  TBA  [TBA]  Default  none  10  0  0   
 F  T  11:30A1:00P  TBA  [TBA]  Default  none  10  0  0   

  01  MWF  3:00P4:00P  TBA  Chen  May 2 2019 6:00PM  8:00PM  60  0  0   

  01  MWF  9:00A10:00A  TBA  McCarthy  May 3 2019 8:00AM  10:00AM  30  0  0   

  01  MWF  3:00P4:00P  TBA  Escobar Vega  May 2 2019 6:00PM  8:00PM  30  0  0   

  07  TBA   TBA  Feres  See Instructor  30  0  0   

  01  MWF  1:00P2:00P  TBA  Chi  May 8 2019 1:00PM  3:00PM  30  0  0   

  01  MWF  10:00A11:00A  TBA  BerchenkoKogan  May 6 2019 10:30AM  12:30PM  50  0  0   

  01  MWF  12:00P1:00P  TBA  Precup  May 8 2019 10:30AM  12:30PM  25  0  0   

 Description:  The course will cover many of the basics of elementary number theory, providing a base from which to approach modern algebra, algebraic number theory and analytic number theory. It will also introduce one of the most important realworld applications of mathematics, namely the use of number theory and algebraic geometry in public
key cryptography. Topics from number theory involve divisibility (Euclidean algorithm, primes, Fundamental Theorem of Arithmetic), congruences (modular arithmetic, Chinese Remainder Theorem, primality testing and factorization). Topics from cryptography will include RSA encryption, DiffieHellman key exchange and elliptic curve cryptography. Topics about algebraic numbers may be include if time permits. Prerequisites: Math 233, 309 and 310 (or permission of instructor)


  01  MWF  11:00A12:00P  TBA  Kerr  Auto Assign Exam Code  30  0  0   

  01  MWF  11:00A12:00P  TBA  Lin  May 7 2019 10:30AM  12:30PM  40  0  0   

  01  MWF  3:00P4:00P  TBA  Wickerhauser  May 2 2019 6:00PM  8:00PM  40  0  0   

  01  TR  11:30A1:00P  TBA  Kuffner  May 6 2019 1:00PM  3:00PM  40  0  0   

  01  MWF  2:00P3:00P  TBA  Gallardo Candela  May 6 2019 3:30PM  5:30PM  75  0  0   

 Description:  Theory of estimation, minimum variance and unbiased estimators, maximum likelihood theory, Bayesian estimation, prior and posterior distributions, confidence intervals for general estimators, standard estimators and distributions such as the Studentt and Fdistribution from a more advanced viewpoint, hypothesis testing, the NeymannPearson Lemma (about best possible tests), linear models, and other topics as time permits. Prereq: Math 3200 and 493, or permission of the instructor. 

  01  MWF  2:00P3:00P  TBA  Chen  May 6 2019 3:30PM  5:30PM  150  0  0   

  01  MWF  1:00P2:00P  TBA  FigueroaLopez  May 8 2019 1:00PM  3:00PM  45  0  0   

